A Perspective: Sparse Compositionality and Efficiently Computable Intelligence
Poggio, T.
CBMM Publication
Publications
2026
Associative Memory as the Core of Intelligence in Technology and Evolution
Poggio, T.
CBMM Publication
2025
Multiplicative Regularization Generalizes Better Than Additive Regularization
Dubach, R., Abdallah, M. S. & Poggio, T.
CBMM Publication
Position: A Theory of Deep Learning Must Include Compositional Sparsity
Danhofer, D. A., D’Ascenzo, D., Dubach, R. & Poggio, T.
CBMM Publication
What if Eye..? Computationally Recreating Vision Evolution
Tiwary, K. et al.
arXiv
2024
Compositional sparsity of learnable functions
Poggio, T. & Fraser, M.
Bulletin of the American Mathematical Society 61, 438-456
Compositional Sparsity of Learnable Functions
Poggio, T. & Fraser, M.
CBMM Publication
For HyperBFs AGOP is a greedy approximation to gradient descent
Gan, Y. & Poggio, T.
CBMM Publication
Formation of Representations in Neural Networks
Ziyin, L., Chuang, I., Galanti, T. & Poggio, T.
CBMM Publication
On Generalization Bounds for Neural Networks with Low Rank Layers
Pinto, A., Rangamani, A. & Poggio, T.
CBMM Publication
On the Power of Decision Trees in Auto-Regressive Language Modeling
Gan, Y., Galanti, T., Poggio, T. & Malach, E.
CBMM Publication
Self-Assembly of a Biologically Plausible Learning Circuit
Liao, Q. et al.
CBMM Publication
2023
A Homogeneous Transformer Architecture
Gan, Y. & Poggio, T.
CBMM Publication
Cervelli menti algoritmi
Poggio, T. & Magrini, M.
272 (Sperling & Kupfer, )
Dynamics in Deep Classifiers trained with the Square Loss: normalization, low rank, neural collapse and generalization bounds
Xu, M., Rangamani, A., Liao, Q., Galanti, T. & Poggio, T.
Research . doi:10.34133/research.0024
Feature learning in deep classifiers through Intermediate Neural Collapse
Rangamani, A., Lindegaard, M., Galanti, T. & Poggio, T.
CBMM Publication
For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability
Rangamani, A., Rosasco, L. & Poggio, T.
Analysis and Applications 21, 193 - 215
How to Guess a Gradient
Singhal, U. et al.
arXiv
Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks
Galanti, T., Xu, M., Galanti, L. & Poggio, T.
CBMM Publication
Norm-based Generalization Bounds for Sparse Neural Networks
Galanti, T., Xu, M., Galanti, L. & Poggio, T.
NeurIPS
SGD and Weight Decay Provably Induce a Low-Rank Bias in Deep Neural Networks
Galanti, T., Siegel, Z., Gupte, A. & Poggio, T.
CBMM Publication
System Identification of Neural Systems: If We Got It Right, Would We Know?
Han, Y., Poggio, T. & Cheung, B.
Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 12430-12444
The Janus effects of SGD vs GD: high noise and low rank
Xu, M. et al.
CBMM Publication
2022
How Deep Sparse Networks Avoid the Curse of Dimensionality: Efficiently Computable Functions are Compositionally Sparse
Poggio, T.
CBMM Publication
PCA as a defense against some adversaries
Gupte, A., Banburski, A. & Poggio, T.
CBMM Publication
Representation Learning in Sensory Cortex: a theory
Anselmi, F. & Poggio, T.
IEEE Access 1 - 1 . doi:10.1109/ACCESS..3208603
SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks
Galanti, T. & Poggio, T.
CBMM Publication
System identification of neural systems: If we got it right, would we know?
Han, Y., Poggio, T. & Cheung, B.
CBMM Publication
2021
Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows
Adler, A., Araya-Polo, M. & Poggio, T.
IEEE Signal Processing Magazine 38, 89 - 119
Distribution of Classification Margins: Are All Data Equal?
Banburski, A., De La Torre, F., Pant, N., Shastri, I. & Poggio, T.
CBMM Publication
Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss
Xu, M. et al.
CBMM Publication
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN
Gant, J., Banburski, A., Deza, A. & Poggio, T.
NeurIPS
From Associative Memories to Powerful Machines
Poggio, T.
CBMM Publication
From Marr’s Vision to the Problem of Human Intelligence
Poggio, T.
CBMM Publication
The Effects of Image Distribution and Task on Adversarial Robustness
Kunhardt, O., Deza, A. & Poggio, T.
CBMM Publication
2020
An Overview of Some Issues in the Theory of Deep Networks
Poggio, T. & Banburski, A.
IEEJ Transactions on Electrical and Electronic Engineering 15, 1560 - 1571
Biologically Inspired Mechanisms for Adversarial Robustness
Reddy, M. Vuyyuru, Banburski, A., Pant, N. & Poggio, T.
CBMM Publication
Complexity Control by Gradient Descent in Deep Networks
Poggio, T., Liao, Q. & Banburski, A.
Nature Communications 11
CUDA-Optimized real-time rendering of a Foveated Visual System
Malkin, E., Deza, A. & Poggio, T.
Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS
Dreaming with ARC
Banburski, A. et al.
Learning Meets Combinatorial Algorithms workshop at NeurIPS
Explicit regularization and implicit bias in deep network classifiers trained with the square loss
Poggio, T. & Liao, Q.
arXiv
For interpolating kernel machines, the minimum norm ERM solution is the most stable
Rangamani, A., Rosasco, L. & Poggio, T.
CBMM Publication
Function approximation by deep networks
Mhaskar, H. & Poggio, T.
Communications on Pure & Applied Analysis 19, 4085 - 4095
Hierarchically Local Tasks and Deep Convolutional Networks
Deza, A., Liao, Q., Banburski, A. & Poggio, T.
CBMM Publication
Implicit dynamic regularization in deep networks
Poggio, T., Liao, Q. & Xu, M.
CBMM Publication
Loss landscape: SGD has a better view
Poggio, T. & Cooper, Y.
CBMM Publication
Scale and translation-invariance for novel objects in human vision
Han, Y., Roig, G., Geiger, G. & Poggio, T.
Scientific Reports 10
Stable Foundations for Learning: a framework for learning theory (in both the classical and modern regime).
Poggio, T.
CBMM Publication
Theoretical issues in deep networks
Poggio, T., Banburski, A. & Liao, Q.
Proceedings of the National Academy of Sciences 201907369 . doi:10.1073/pnas.1907369117
2019
Biologically-plausible learning algorithms can scale to large datasets.
Xiao, W., Chen, H., Liao, Q. & Poggio, T.
International Conference on Learning Representations, (ICLR )
Deep Recurrent Architectures for Seismic Tomography
Adler, A., Araya-Polo, M. & Poggio, T.
81st EAGE Conference and Exhibition
Double descent in the condition number
Poggio, T., Kur, G. & Banburski, A.
CBMM Publication
Dynamics & Generalization in Deep Networks -Minimizing the Norm
Banburski, A. et al.
NAS Sackler Colloquium on Science of Deep Learning
Eccentricity Dependent Neural Network with Recurrent Attention for Scale, Translation and Clutter Invariance
Zhang, J., Han, Y., Poggio, T. & Roig, G.
Vision Science Society
Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks
Han, Y., Roig, G., Geiger, G. & Poggio, T.
Vision Science Society
Properties of invariant object recognition in human oneshot learning suggests a hierarchical architecture different from deep convolutional neural networks
Han, Y., Roig, G., Geiger, G. & Poggio, T.
Vision Science Society . doi:10.1167/19.10.28d
Theoretical Issues in Deep Networks
Poggio, T., Banburski, A. & Liao, Q.
CBMM Publication
Theories of Deep Learning: Approximation, Optimization and Generalization
Liao, Q., Banburski, A. & Poggio, T.
TECHCON
Weight and Batch Normalization implement Classical Generalization Bounds
Banburski, A. et al.
ICML
2018
A fast, invariant representation for human action in the visual system
Isik, L., Tacchetti, A. & Poggio, T.
Journal of Neurophysiology . doi:https://doi.org/10.1152/jn.00642.2017
An analysis of training and generalization errors in shallow and deep networks
Mhaskar, H. & Poggio, T.
CBMM Publication
Biologically-plausible learning algorithms can scale to large datasets
Xiao, W., Chen, H., Liao, Q. & Poggio, T.
CBMM Publication
Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?
Villalobos, K. M. et al.
CBMM Publication
Classical generalization bounds are surprisingly tight for Deep Networks
Liao, Q., Miranda, B., Hidary, J. & Poggio, T.
CBMM Publication
Invariant Recognition Shapes Neural Representations of Visual Input
Tacchetti, A., Isik, L. & Poggio, T.
Annual Review of Vision Science 4, 403 - 422
Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results
Arend, L. et al.
CBMM Publication
Theory I: Deep networks and the curse of dimensionality
Poggio, T. & Liao, Q.
Bulletin of the Polish Academy of Sciences: Technical Sciences 66
Theory II: Deep learning and optimization
Poggio, T. & Liao, Q.
Bulletin of the Polish Academy of Sciences: Technical Sciences 66
Theory III: Dynamics and Generalization in Deep Networks
Banburski, A. et al.
CBMM Publication
2017
A fast, invariant representation for human action in the visual system.
Isik, L., Tacchetti, A. & Poggio, T.
J Neurophysiol jn.00642. . doi:10.1152/jn.00642
Compression of Deep Neural Networks for Image Instance Retrieval
Chandrasekhar, V. et al.
CBMM Publication
Do Deep Neural Networks Suffer from Crowding?
Volokitin, A., Roig, G. & Poggio, T.
CBMM Publication
Eccentricity Dependent Deep Neural Networks for Modeling Human Vision
Roig, G., Chen, F., Boix, X. & Poggio, T.
Vision Sciences Society
Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision
Chen, F., Roig, G., Isik, L., Boix, X. & Poggio, T.
AAAI Spring Symposium Series, Science of Intelligence
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
Liang, T., Poggio, T., Rakhlin, A. & Stokes, J.
arXiv.org
Invariant action recognition dataset
Tacchetti, A., Isik, L. & Poggio, T.
CBMM Publication
Invariant recognition drives neural representations of action sequences
Tacchetti, A., Isik, L. & Poggio, T.
PLOS Computational Biology 13, e1005859
Is the Human Visual System Invariant to Translation and Scale?
Han, Y., Roig, G., Geiger, G. & Poggio, T.
AAAI Spring Symposium Series, Science of Intelligence
Musings on Deep Learning: Properties of SGD
Zhang, C. et al.
CBMM Publication
Object-Oriented Deep Learning
Liao, Q. & Poggio, T.
CBMM Publication
On the Human Visual System Invariance to Translation and Scale
Han, Y., Roig, G., Geiger, G. & Poggio, T.
Vision Sciences Society
Pruning Convolutional Neural Networks for Image Instance Retrieval
Manek, G. et al.
CBMM Publication
Representation Learning from Orbit Sets for One-shot Classification
Tacchetti, A., Voinea, S., Evangelopoulos, G. & Poggio, T.
AAAI Spring Symposium Series, Science of Intelligence
Symmetry Regularization
Anselmi, F., Evangelopoulos, G., Rosasco, L. & Poggio, T.
CBMM Publication
Theory II: Landscape of the Empirical Risk in Deep Learning
Poggio, T. & Liao, Q.
CBMM Publication
Theory of Deep Learning IIb: Optimization Properties of SGD
Zhang, C. et al.
CBMM Publication
Theory of Deep Learning III: explaining the non-overfitting puzzle
Poggio, T. et al.
CBMM Publication
View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation
Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A. & Poggio, T.
Current Biology 27, 1-6
When and Why Are Deep Networks Better Than Shallow Ones?
Mhaskar, H., Liao, Q. & Poggio, T.
AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q.
International Journal of Automation and Computing 1-17 . doi:10.1007/s11633-017-1054-2
2016
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
Liao, Q. & Poggio, T.
CBMM Publication
Deep Leaning: Mathematics and Neuroscience
Poggio, T.
A Sponsored Supplement to Science Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience, 9-12
Deep Learning: mathematics and neuroscience
Poggio, T.
CBMM Publication
Deep vs. shallow networks : An approximation theory perspective
Mhaskar, H. & Poggio, T.
CBMM Publication
Deep vs. shallow networks: An approximation theory perspective
Mhaskar, H. & Poggio, T.
Analysis and Applications 14, 829 - 848
Fast, invariant representation for human action in the visual system
Isik, L., Tacchetti, A. & Poggio, T.
CBMM Publication
Foveation-based Mechanisms Alleviate Adversarial Examples
Luo, Y., Boix, X., Roig, G., Poggio, T. & Zhao, Q.
CBMM Publication
Group Invariant Deep Representations for Image Instance Retrieval
Morère, O. et al.
CBMM Publication
How Important Is Weight Symmetry in Backpropagation?
Liao, Q., Leibo, J. Z. & Poggio, T.
Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)
Introduction Special issue: Deep learning
Bach, F. & Poggio, T.
Information and Inference 5, 103-104
Learning Functions: When Is Deep Better Than Shallow
Mhaskar, H., Liao, Q. & Poggio, T.
CBMM Publication
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
Morère, O., Veillard, A., Chandrasekhar, V. & Poggio, T.
arXiv.org
Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing
Tan, C. & Poggio, T.
Public Library of Science | PLoS ONE 1(3): e0150980
On invariance and selectivity in representation learning
Anselmi, F., Rosasco, L. & Poggio, T.
Information and Inference: A Journal of the IMA iaw009 . doi:10.1093/imaiai/iaw009
Spatio-temporal convolutional networks explain neural representations of human actions
Tacchetti, A., Isik, L. & Poggio, T.
CBMM Publication
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning
Liao, Q., Kawaguchi, K. & Poggio, T.
CBMM Publication
Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q.
CBMM Publication
Turing++ Questions: A Test for the Science of (Human) Intelligence.
Poggio, T. & Meyers, E.
AI Magazine 37 , 73-77
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
Leibo, J. Z., Liao, Q., Freiwald, W. A., Anselmi, F. & Poggio, T.
CBMM Publication
Visual Cortex and Deep Networks: Learning Invariant Representations
Poggio, T. & Anselmi, F.
136 (The MIT Press, )
2015
A Science of Intelligence
Koch, C. & Poggio, T.
CBMM Publication
Deep Convolutional Networks are Hierarchical Kernel Machines
Anselmi, F., Rosasco, L., Tan, C. & Poggio, T.
CBMM Publication
Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations
Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. & Poggio, T.
INTERSPEECH- (International Speech Communication Association (ISCA), )
Holographic Embeddings of Knowledge Graphs
Nickel, M., Rosasco, L. & Poggio, T.
CBMM Publication
How Important is Weight Symmetry in Backpropagation?
Liao, Q., Leibo, J. Z. & Poggio, T.
CBMM Publication
I-theory on depth vs width: hierarchical function composition
Poggio, T., Anselmi, F. & Rosasco, L.
CBMM Publication
Invariant representations for action recognition in the visual system
Isik, L., Tacchetti, A. & Poggio, T.
Computational and Systems Neuroscience
Invariant representations for action recognition in the visual system.
Tacchetti, A., Isik, L. & Poggio, T.
Vision Sciences Society 15
Learning with a Wasserstein Loss
Frogner, C., Zhang, C., Mobahi, H., Araya-Polo, M. & Poggio, T.
Advances in Neural Information Processing Systems (NIPS ) 28
Learning with Group Invariant Features: A Kernel Perspective
Mroueh, Y., Voinea, S. & Poggio, T.
NIPS
Notes on Hierarchical Splines, DCLNs and i-theory
Poggio, T., Rosasco, L., Shashua, A., Cohen, N. & Anselmi, F.
CBMM Publication
On Invariance and Selectivity in Representation Learning
Anselmi, F., Rosasco, L. & Poggio, T.
CBMM Publication
Unsupervised learning of invariant representations
Anselmi, F. et al.
Theoretical Computer Science . doi:10.1016/j.tcs..06.048
What if..
Poggio, T.
CBMM Publication
2014
A Deep Representation for Invariance and Music Classification
Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. & Poggio, T.
ICASSP - IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, ). doi:10.1109/ICASSP..6854954
A Deep Representation for Invariance And Music Classification
Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. & Poggio, T.
CBMM Publication
Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?
Liao, Q., Leibo, J. Z., Mroueh, Y. & Poggio, T.
CBMM Publication
Computational role of eccentricity dependent cortical magnification.
Poggio, T., Mutch, J. & Isik, L.
CBMM Publication
Is Research in Intelligence an Existential Risk?
Poggio, T.
CBMM Publication
Learning An Invariant Speech Representation
Evangelopoulos, G., Voinea, S., Zhang, C., Rosasco, L. & Poggio, T.
CBMM Publication
Learning invariant representations and applications to face verification
Liao, Q., Leibo, J. Z. & Poggio, T.
NIPS 2013 (Advances in Neural Information Processing Systems 26, )
Neural tuning size is a key factor underlying holistic face processing.
Tan, C. & Poggio, T.
CBMM Publication
Phone Classification by a Hierarchy of Invariant Representation Layers
Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. & Poggio, T.
INTERSPEECH - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), )
Representation Learning in Sensory Cortex: a theory.
Anselmi, F. & Poggio, T.
CBMM Publication
Speech Representations based on a Theory for Learning Invariances
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T.
CBMM Publication
Subtasks of unconstrained face recognition
Leibo, J. Z., Liao, Q. & Poggio, T.
CBMM Publication
Subtasks of Unconstrained Face Recognition
Leibo, J. Z., Liao, Q. & Poggio, T.
CBMM Publication
The dynamics of invariant object recognition in the human visual system.
Isik, L., Meyers, E., Leibo, J. Z. & Poggio, T.
J Neurophysiol 111, 91-102
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex
Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T.
doi:10.1101/004473
Unsupervised learning of clutter-resistant visual representations from natural videos.
Liao, Q., Leibo, J. Z. & Poggio, T.
CBMM Publication
Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?
Anselmi, F. et al.
CBMM Publication
Word-level Invariant Representations From Acoustic Waveforms
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T.
INTERSPEECH - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), )
2013
NSF Science and Technology Centers – The Class of 2013
Lattman, E., Poggio, T. & Westervelt, R.
CBMM Publication
Unknown Title
Villa, S. et al.
Empirical Inference 59 - 69 (Springer Berlin Heidelberg, ). doi:10.1007/978-3-642-41136-610.1007/978-3-642-41136-6_7
Unsupervised Learning of Invariant Representations in Hierarchical Architectures.
Anselmi, F. et al.
CBMM Publication
2012
Duals of Orphan-Free Anisotropic Voronoi Diagrams are Embedded Meshes.
Canas G. D., S. J. Gortler
28th ACM Symposium on Computational Geometry, 2012
GURLS: a Toolbox for Regularized Least Squares Learning
Tacchetti, A., P. Mallapragada, M. Santoro, and L. Rosasco
MIT-CSAIL-TR-2012-003, CBCL-306, Massachusetts Institute of Technology, Cambridge, MA, January, 2012Previous version: GURLS: a Toolbox for Large Scale Multiclass Learning, Workshop: “Big Learning: Algorithms, Systems, and Tools for Learning at Scale” at NIPS 2011, December 16-17 2011, Sierra Nevada, Spain
Incorporation of new information into prefrontal cortical activity after learning working memory tasks
Meyers, E., X.L.Qi and C. Constantinidis
Proceedings of the National Academy of Sciences, 109:4651-4656-8855, 2012
Kernels for Vector-Valued Functions: a Review.
Alvarez M., N. Lawrence, L. Rosasco
Foundations and Trends in Machine Learning 4(3):195-266, 2012
Learning and disrupting invariance in visual recognition with a temporal association rule
Isik, L.*, J.Z. Leibo* and T. Poggio
Front. Comput. Neurosci. 6:37. doi: 10.3389/fncom.2012.00037, June 25, 2012*These authors contributed equally to this work
Learning manifolds with k-means and k-flats
Canas, G. D., Poggio, T. & Rosasco, L.
Advances in Neural Information Processing Systems 25 (NIPS )
Learning Manifolds with K-Means and K-Flats.
Canas G. D., T. Poggio, L. Rosasco
Advances in Neural Information Processing Systems, Lake Tahoe, December 2012
Learning Probability Measures with respect to Optimal Transport Metrics.
Canas G. D., L. Rosasco
Advances in Neural Information Processing Systems, Lake Tahoe, December 2012
Multi-class Learning with Simplex Coding.
Mroueh Y., T. Poggio, L. Rosasco, and J. J. Slotine
Advances in Neural Information Processing Systems, Lake Tahoe, December 2012
Multiscale Geometric Methods for Data Sets I:Multiscale SVD, Noise and Curvature.
Little A. V., M. Maggioni, and L. Rosasco
MIT-CSAIL-TR-2012-029 ,CBCL-310, Massachusetts Institute of Technology, Cambridge, MA, September 2012
Practical Conditions for Well-behaved-ness of Anisotropic Voronoi Diagrams.
Canas, G. D
Arxiv 1202.0867
Preliminary MEG decoding results
Isik, L., E.M. Meyers, J.Z. Leibo, and T. Poggio
MIT-CSAIL-TR-2012-010,CBCL-307, Massachusetts Institute of Technology, Cambridge, MA, April 20, 2012
The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work)
Poggio T., J. Mutch, F. Anselmi, L. Rosasco, J.Z. Leibo, and A. Tacchetti
MIT-CSAIL-TR-2012-035, Massachusetts Institute of Technology, Cambridge, MA, December 29, 2012
The Levels of Understanding framework, revised
Poggio, T
Perception, volume 41, pages 1017 – 1023, December 2012 doi:10.1068/p7299
Throwing Down the Visual Intelligence Gauntlet.
Tan, C., J.Z. Leibo, and T. Poggio
Machine Learning for Computer Vision; eds Cipolla R., Battiato S., Farinella G.M., Springer: Studies in Computational Intelligence Vol. 411. July, 2012
2011
A hierarchical model of peripheral vision
Isik, L., J.Z. Leibo, J. Mutch, S.W. Lee, and T Poggio
MIT-CSAIL-TR-2011-031/CBCL-300, Massachusetts Institute of Technology, Cambridge, MA, June 2011
A Large Video Database for Human Motion Recognition
Garrote, E., Jhuang, H., Huehne, H., Poggio, T. & Serre, T.
CBMM Publication
doi: 10.1509/jmkr.48.3.497
Dahan, E., A.J. Kim, A.W. Lo, T. Poggio, and N. Chan, Securities Trading of Concepts (STOC) Journal of Marketing Research: Vol. 48, No. 3, pp. 497-517
2011
Dorsal Stream: From Algorithm to Neuroscience
Jhuang, H. (Ph.D. Thesis, EECS, MIT, March 2011):
HMDB: A Large Video Database for Human Motion Recognition
Kuehne, T., H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “
” ICCV 2011, Click here for software documentation
How can cells in the anterior medial face patch be viewpoint invariant?
Leibo, J.Z., J. Mutch and T Poggio
Presented at COSYNE 2011, Salt Lake City, UT. Available from Nature Precedings at dx.doi.org/10.1038/npre.2011.5845.1
Kernels for Vector-Valued Functions: a Review
Alvarez, M.A., L. Rosasco and N. D. Lawrence
MIT-CSAIL-TR-2011-033/CBCL-301 Massachusetts Institute of Technology, Cambridge, MA, June 30, 2011
Learning and disrupting invariance in visual recognition
Isik, L., J.Z. Leibo and T. Poggio
MIT-CSAIL-TR-2011-040/CBCL-302, Massachusetts Institute of Technology, Cambridge, MA, September 10, 2011
Learning to discount transformations as the computational goal of visual cortex
Leibo, J.Z., J. Mutch andT Poggio
Presented at FGVC/CVPR 2011, Colorado Springs, CO. Available from Nature Precedings at dx.doi.org/10.1038/npre.2011.6078.1
MIT-CSAIL-TR-2011-021/CBCL-298
Chikkerur, S. and T. Poggio, Approximations in the HMAX Model
Massachusetts Institute of Technology, Cambridge, MA, April 14, 2011
Multi-Output Learning via Spectral Filtering
Baldassarre, L., L. Rosasco, A. Barla, and A. Verri
MIT-CSAIL-TR-2011-004/CBCL-296, Massachusetts Institute of Technology, Cambridge, MA, January 24, 2011
Nonparametric Sparsity and Regularization
Mosci, S., L Rosasco, M. Santoro, A. Verri, and S. Villa
MIT-CSAIL-TR-2011-041/CBCL-303 Massachusetts Institute of Technology, Cambridge, MA, September 2011
Object decoding with attention in inferior temporal cortex
Zhang Y.*, E. Meyers *, N. Bichot, T. Serre, T. Poggio, and R. Desimone
PNAS Proceedings of the National Academy of Sciences, 108:8850-8855, 2011; Published online before print, May 9, 2011, doi: 10.1073/pnas.1100999108. *These authors contributed equally
Online Learning, Stability, and Stochastic Gradient Descent
Poggio, T., S. Voinea and L. Rosasco
Cornell University Library, arXiv:1105.4701v2 [cs.LG], May 25, 2011
Regularization Predicts While Discovering Taxonomy
Mroueh, Y., T. Poggio and L. Rosasco
MIT-CSAIL-TR-2011-029/CBCL-299, Massachusetts Institute of Technology, Cambridge, MA, June 3, 2011
The Computational Magic of the Ventral Stream: Towards a Theory
Poggio, T. (sections with J. Mutch, J.Z. Leibo and L. Rosasco)
Nature Precedings, doi:10.1038/npre.2011.6117.1 July 16, 2011
Tutorial on Pattern Classification in Cell Recording.
Meyers, E., Kreiman, G
In: Visual population codes. Kreigeskorte, N., and Kreiman, G. (eds.), 2011, MIT Press
Werner Reichardt: the man and his scientific legacy
Poggio, T. and G. Geiger
MIT-CSAIL-TR-2011-011, CBCL-297, Massachusetts Institute of Technology, Cambridge, MA, March 4, 2011
Why The Brain Separates Face Recognition From Object Recognition
Leibo, J.Z., Mutch J., and T. Poggio
Advances in Neural Information Processing Systems, Granada Spain, December 2011
2010
A Neuromorphic Approach to Computer Vision
Serre, T. and T. Poggio “
”, Communications of the ACM (online), Vol . 53, No. 10, October 2010 [doi :10.1145/1831407.1831425]
A primal-dual algorithm for group sparse regularization with overlapping groups
Mosci, S., A. Villa, A. Verri, and L. Rosasco
Advances in Neural Information Processing Systems (NIPS 2010). December 2010
Automated home-cage behavioural phenotyping of mice
Jhuang, H., E. Garrote, J. Mutch, X. Yu, V. Khilnani, T. Poggio, A.D. Steele, and T. Serre
Nature Communications,” 1, Article 68, [doi: 10.1038/ncomms1064], September 7, 2010. Click here for software documentation
CNS (“Cortical Network Simulator”): a GPU-based framework for simulating cortically-organized networks
Mutch, J., Knoblich, U. & Poggio, T.
CBMM Publication
CNS: a GPU-based framework for simulating cortically-organized networks
Mutch, J., U. Knoblich and T. Poggio
MIT-CSAIL-TR-2010-013 / CBCL-286, Massachusetts Institute of Technology, Cambridge, MA, February 26, 2010
Examining high level neural representations of cluttered scenes
Meyers, E., Embark, H., Freiwald, W., Serre, T., Kreiman, G., and Poggio T
MIT-CSAIL-TR-2010-034 / CBCL-289, Massachusetts Institute of Technology, Cambridge, MA, July 29, 2010
From primal templates to invariant recognition
Leibo, J.Z., J. Mutch, S. Ullman, and T. Poggio
MIT-CSAIL-TR-2010-057/CBCL-293, Massachusetts Institute of Technology, Cambridge, MA, December 4, 2010
Generalization and Properties of the Neural Response
Bouvrie, J., T. Poggio, R. Lorenzo, S. Smale, and A. Wibisono
MIT-CSAIL-TR-2010-051/CBCL-292, Massachusetts Institute of Technology, Cambridge, MA, November 19, 2010
Learning and Invariance in a Family of Hierarchical Kernels
Wibisono, A., J. Bouvrie, L. Rosasco, and T. Poggio, “
“, MIT-CSAIL-TR-2010-035 / CBCL-290, Massachusetts Institute of Technology, Cambridge, MA, July 30, 2010
Learning Generic Invariances in Object Recognition: Translation and Scale
Leibo, J.Z., J. Mutch, L. Rosasco, S. Ullman, and T. Poggio
MIT-CSAIL-TR-2010-061/CBCL-294, Massachusetts Institute of Technology, Cambridge, MA, December 30, 2010
Neurons That Confuse Mirror-Symmetric Object Views
Mutch, J., J.Z. Leibo, S. Smale, L. Rosasco, and T. Poggio
MIT-CSAIL-TR-2010-062/CBCL-295, Massachusetts Institute of Technology, Cambridge, MA, December 31, 2010
On Learning with Integral Operators
Rosasco, L., Belkin, M., De Vito, E
Journal of Machine Learning Research, 11, (2010) 905-934 Feb 2010
Prefrontal cortex activity during flexible categorization
Roy, J.E., Riesenhuber, M., T. Poggio and E.K. Miller, “
“. Journal of Neuroscience, 30:8519-8528, 2010
Reading the mind’s eye: Decoding object information during mental imagery from fMRI patterns.
Reddy, L., N. Tsuchyia and T. Serre, “
” Neuroimage, 50, pp. 818-825, Feb 2010.doi:10.1167/9.8.782
Spectral Regularization for Support Estimation
De Vito, E., L. Rosasco, and A. Toigo
Advances in Neural Information Processing Systems (NIPS 2010). December 2010
System for Mouse Behavior Recognition
Garrote, E. et al.
CBMM Publication
What and Where: A Bayesian inference theory of visual attention
Chikkerur, S., T. Serre, C. Tan, and T. Poggio
Vision Research, [doi: 10.1016 /j.visres.2010.05.013], May 20, 2010
2009
“A Bayesian inference theory of attention: neuroscience and algorithms”
Chikkerur, S., T. Serre, and T. Poggio
MIT-CSAIL-TR-2009-047/CBCL-280, Massachusetts Institute of Technology, Cambridge, MA, October 3, 2009
“An integrated model of visual attention using shape-based features”
Chikkerur, S., C. Tan, T. Serre, and T. Poggio
MIT-CSAIL-TR-2009-029 / CBCL-278, Massachusetts Institute of Technology, Cambridge, MA, June 20, 2009
“Attentive processing improves object recognition”
Chikkerur, S., T. Serre, and T. Poggio
MIT-CSAIL-TR-2009-046 /CBCL-279, Massachusetts Institute of Technology, Cambridge, MA, October 2, 2009
“Elastic-Net Regularization in Learning Theory
De Mol, C., E. De Vito and L. Rosasco
“, to be published in the Journal of Complexity, available online January 30, 2009
“Fully Automatic Calibration of LIDAR and Video Streams From a Vehicle”
Bileschi, S
3-D Digital Imaging and Modeling (3DIM ICCV Workshop), 2009
“Object Recognition Using Boosted Oriented Filter Based Local Descriptors.”
Yokono, J.J., and T.Poggio
IEEJ Transactions on Electronics, Information and Systems 129 5 (2009): 806-11+5, 2009
“Scene Classification with a Biologically Inspired Method”
Terashima, Y
CBCL paper #277/CSAIL Technical Report#2009-020, CBCL-277 Massachusetts Institute of Technology, Cambridge, MA, May 10, 2009
“Sufficient Conditions for Uniform Stability of Regularization Algorithms”
Wibisono, A., L. Rosasco, T. Poggio
CBCL paper #284/CSAIL Technical Report#MIT-CSAIL-TR-2009-060, Massachusetts Institute of Technology, Cambridge, MA, December 1, 2009
10.3949/ccjm.76.s2.16
Cao, R., B.T. Higashikubo, J.A. Cardin, U. Knoblich, R. Ramos, M.T. Nelson, C.I. Moore and J.C. Brumberg. “Pinacidil induces vascular dilation and hyperemia in vivo and does not impact biophysical properties of neurons and astrocytes in vitro.” Cleve. Clin. J. Med. 76, 2009 [doi:
]
Automated home-cage behavioral phenotyping of mice
Yu, X., A. Steele, V. Khilnani, E. Garrote, H. Jhuang, T. Serre, and T. Poggio, “
MIT-CSAIL-TR-2009-052 CBCL-283, Massachusetts Institute of Technology, Cambridge, MA, October 26, 2009
Driving fast-spiking cells induces gamma rhythm and controls sensory responses.
Cardin, J.A., M. Carlén, K. Meletis, U. Knoblich, F. Zhang, K. Deisseroth, L.H. Tsai and C.I. Moore. “
” Nature 459(7247):663-667, 2009 [doi: 10.1038/nature08002]
Iterative Projection Methods for Structured Sparsity Regularization
Rosasco, L., S. Mosci, M. Santoro, A. Verri, and S. Villa, “
“, MIT-CSAIL-TR-2009-50 / CBCL-282, Massachusetts Institute of Technology, Cambridge, MA, October 14, 2009
Notes on the Shannon Entropy of the Neural Response
Bouvrie, J., L. Rosasco, G. Shakhnarovich, and S. Smale, “
“, CBCL-281, MIT-CSAIL-TR-2009-049, Massachusetts Institute of Technology, Cambridge, MA, October 9, 2009
On Invariance in Hierarchical Models
Bouvrie, J. , L. Rosasco, and T. Poggio. “
“. Advances in Neural Information Processing Systems (NIPS) 22, 2009
Visual Parsing After Recovery From Blindness.
Ostrovsky, Y., Meyers, E., Ganesh, S., Mathur, U., Sinha P. ”
” Psychological Science, 20:1467-1491, 2009
2008
” Finding Structured Gene Signatures”
Mosci, S.; A. Barla; A. Verri and L. Rosasco
Proc. of IEEE BIBM, Philadelphia, PA. USA, 2008
“Discriminative Word-Spotting Using Ordered Spectro-Temporal Patch Features”
Ezzat, T. and T. Poggio
SAPA Workshop, Interspeech, Brisbane, Australia, 2008
“Spectral Algorithms for Supervised Learning”
Lo Gerfo L., Rosasco L., Odone F., De Vito E. and Verri, A
Neural Computation, 20: 1873-1897, 2008
“Wide and diffuse perceptual modes characterize dyslexics in vision and audition
Geiger, G., C. Cattaneo, R. Galli, U. Pozzoli, M. Lorusso, A. Facoetti, and M. Molteni
“, Perception Vol. 37, Issue 11, Pages 1745 – 1764, November 17, 2008
A Canonical Neural Circuit for Cortical Nonlinear Operations
Kouh, M. and T. Poggio. “
” Neural ComputationVol. 20, No. 6, Pages 1427-1451, June 2008
A method for robust variable selection with significance assessments
Barla, A., Mosci, S., Rosasco, L. and Verri, A. “
” 16th European Symposium on Artificial Neural Networks
A Multi-Scale Generalization of the HoG and HMAX Image Descriptors for Object Detection
Bileschi, S.M
CBCL paper #271/CSAIL Technical Report #2008-019, Massachusetts Institute of Technology, Cambridge, MA, April 9, 2008
A Note on Perturbation Results for Learning Empirical Operators
Rosasco, L., M. Belkin, and E. De Vito. “
“, CBCL paper #274/ CSAIL Technical Report #TR-2008-052 , Massachusetts Institute of Technology, Cambridge, MA, August 19, 2008
Adaptive Kernel Methods Using the Balancing Principle
De Vito, E., S. Pereverzyev, and L. Rosasco. “
“, CBCL paper #275/CSAIL Technical Report#TR-2008-062Massachusetts Institute of Technology, Cambridge, MA, October 16, 2008
Dynamic Population Coding of Category Information in Inferior Temporal and Prefrontal Cortex”
Meyers, E.M., D. J. Freedman, G. Kreiman, E.K. Miller, and T. Poggio. “
Journal of Neurophysiology Vol. 100: 1407-1419, June 18, 2008
Elastic-Net Regularization in Learning Theory
De Mol, C., E. De Vito and L. Rosasco. “
“, CBCL paper #273/ CSAIL Technical Report #TR-2008-046 , Massachusetts Institute of Technology, Cambridge, MA, July 24, 2008
Localized Spectro-Temporal Cepstral Analysis of Speech
Bouvrie, J., T. Ezzat, and T. Poggio. “
“, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, 2008
Mathematics of the Neural Response
S. Smale, L. Rosasco, J. Bouvrie, A. Caponnetto, and T. Poggio. “
“, CBCL Paper #276/MIT CSAIL Technical Report #TR2008-070, Massachusetts Institute of Technology, Cambridge, MA, November, 2008
Object Detection at Multiple Scales Improves Accuracy
Bileschi, S.M
ICPR 2008
Object Recognition, Computer Vision, and the Caltech 101: A Response to Pinto et al.
LeCun, Y., D.G. Lowe, J. Malik, J. Mutch, P. Perona, and T. Poggio
PLoS Computational Biology, Posted Online March 2008
On a model of visual cortex: learning invariance and selectivity from image sequences
Caponnetto, A., T. Poggio and S. Smale
CBCL paper#272/CSAIL Technical Report #2008-030 , Massachusetts Institute of Technology, Cambridge, MA, April 4, 2008
Using Biologically Inspired Visual Features for Face Processing
Meyers, E., and L. Wolf
International Journal of Computer Vision, Vol 76, No. 1, 93-104, 2008
2007
. “Memories of a friend of Odile” in Odile Crick: A Memorial Exhibition
Poggio, T
curator Becky Cohen, The Salk Institute, 2004
. Trade-off between object selectivity and tolerance in monkey inferotemporal cortex
Zoccolan, D., M. Kouh, T. Poggio, and James J. DiCarlo
Journal of Neuroscience, Volume 27(45), pp.12292–12307, November, 2007
“Image representations beyond histograms of gradients: The role of Gestalt descriptors”
Bileschi, S. Wolf, L
Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2007 – CVPR ’07, June 2007, Pages 1-8, Digital Object Identifier 10.1109/CVPR.2007.383122
A Biologically Inspired System for Action Recognition
Jhuang H., T. Serre, L. Wolf and T. Poggio
In: Proceedings of the Eleventh IEEE International Conference on Computer Vision (ICCV), 2007
A Component-based Framework for Face Detection and Identification
Heisele, B., T. Serre and T. Poggio
International Journal of Computer Vision, 74(2), pp. 167-181, 2007
A Feedforward Architecture Accounts for Rapid Categorization
Serre, T., A. Oliva and T. Poggio
Proceedings of the National Academy of Sciences (PNAS), Vol. 104, No. 15, 6424-6429, 2007
A Model of V4 Shape Selectivity and Invariance
Cadieu, C., M. Kouh, A. Pasupathy, C. Connor, M. Riesenhuber, and T. Poggio
Journal of Neurophysiology, Vol. 98, 1733-1750, June, 2007
A quantitative theory of immediate visual recognition
Serre, T., G. Kreiman, M. Kouh, C. Cadieu, U. Knoblich and T. Poggio
Progress in Brain ResearchVol. 165, 33-56, 2007
AM-FM Demodulation of Spectrograms using 2-D Max-Gabor Analysis
Ezzat, T., J. Bouvrie, and T. Poggio
ICASSP, Hawaii, 2007
Derived Distance: towards a mathematical theory of visual cortex,
Smale, S., T. Poggio, A. Caponnetto, and J. Bouvrie
CBCL Paper, Massachusetts Institute of Technology, Cambridge, MA, November, 2007
How the Brain Might Work: The Role of Information and Learning in Understanding and Replicating Intelligence
Poggio. T
In: Information: Science and Technology for the New Century, Editors: G. Jacovitt, A. Pettorossi, R. Consolo and V. Senni, Lateran University Press, Quaderni Sefir, 7, pp. 45-61, 2007
Learning complex cell invariance from natural videos: A plausibility proof,
Masquelier, T., T. Serre, S. Thorpe and T. Poggio
CBCL Paper #269/AI Technical Report #2007-060, Massachusetts Institute of Technology, Cambridge, MA, December, 2007
Modeling Appearances with Low-Rank SVM,
Wolf, L., H.Jhuang and T.Hazan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007
Noise Robust Phonetic Classification with Linear Regularized Least Squares and Second-Order Features
Rifkin, R., K. Schutte, D. Saad, J. Bouvrie, and J. Glass
ICASSP, Honolulu, 2007
Notes on Regularized Least-Squares
Rifkin, R.,. and R.A. Lippert
CBCL Paper #268/AI Technical Report #2007-019, Massachusetts Institute of Technology, Cambridge, MA, May, 2007
Phonetic Classification Using Hierarchical, Feed-forward, Spectro-temporal Patch-based Architectures
Rifkin, R., J. Bouvrie, K. Schutte, S. Chikkerur, M. Kouh, T. Ezzat and T. Poggio
CBCL Paper #267/AI Technical Report #2007-019, Massachusetts Institute of Technology, Cambridge, MA, March, 2007
Robust Object Recognition with Cortex-like Mechanisms
Serre, T., L. Wolf, S. Bileschi, M. Riesenhuber and T. Poggio
IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 3, 411-426, 2007
Spectro-Temporal Analysis of Speech Using 2-D Gabor Filters
Ezzat, T., J. Bouvrie, and T. Poggio
Interspeech, Belgium 2007
Visual object concept discovery: Observations in congenitally blind children, and a computational approach,
Bouvrie, J. and P. Sinha
Neurocomputing, Volume 70, Issues 13-15, pp.2218-2233, 2007
2006
A Critical View of Context
Wolf, L. and S. Bileschi
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006
A Multiview Face Identification Model With No Geometric Constraints
Yokono, J.J. and T. Poggio
Sony Intelligence Dynamics Laboratories, Inc. March, 2006
A Unified System for Object Detection, Texture Recognition and Context Analysis Based on the Standard Model Feature Set
Bileschi, S. and L. Wolf
In: British Machine Vision Conference (BMVC), 2006
Adaptation for Regularization Operators in Learning Theory
Caponnetto, A. and Y. Yao
CBCL Paper #265/AI Technical Report #063, Massachusetts Institute of Technology, Cambridge, MA, September, 2006
An Incremental Algorithm for Signal Reconstruction from Short-time Fourier Transform Magnitude
Bouvrie, J. and T. Ezzat
In: Ninth International Conference on Spoken Language Processing (ICSLP-Pittsburgh, PA), 2006
Applications of Empirical Processes in Learning Theory: Algorithmic Stability and Generalization Bounds
Rakhlin, Alexander (Ph.D. Thesis, BCS, MIT, April 2006):
Biophysical Models of Neural Computation: Max and Tuning Circuits
Knoblich, U., J. Bouvrie, and T. Poggio
In: Web Intelligence Meets Brain Informatics, First WICI International Workshop (WImBI 2006) Bejing, China, December 2006
Dealers, Insiders and Bandits: Learning and Its Effects on Market Outcomes
Das, Sanmay (Ph.D. Thesis, EECS, MIT, April 2006):
Distinct Gene Systems Mediating Activity-dependent Plasticity in Visual Cortex
Tropea, D., G. Kreiman, A. Lyckman, S. Mukherjee, H. Yu, S. Horng and M. Sur
Nature Neuroscience, 9, 660-668, 2006
Empirical Comparison between Hierarchical Fragments Based and Standard Model Based Object Recognition Systems
Chikkerur, S. and L. Wolf
CBCL Paper #MMVI-0I, Massachusetts Institute of Technology, Cambridge, MA, March, 2006
Experience dependent sharpening of visual shape selectivity in inferior temporal cortex,
Freedman, D.J., Riesenhuber, M., Poggio, T., and Miller, E.K
Cerebral Cortex, 16: 1631-1644, 2006
Human Document Classification Using Bags of Words
Wolf F., T. Poggio, P. Sinah
CBCL paper #263/CSAIL Technical Report #2006-054, Massachusetts Institute of Technology, Cambridge, MA, August 9, 2006
Learning a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machines
Serre, T
CBCL Paper #260/AI Technical Report #028, Massachusetts Institute of Technology, Cambridge, MA, March, 2006
Learning Theory: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization
Mukherjee, S., P. Niyogi, T. Poggio and R. Rifkin
Advances in Computational Mathematics, 25, 161-193, 2006
Max-Gabor Analysis and Synthesis of Spectrograms
Ezzat, T., J. Bouvrie and T. Poggio
In: Ninth International Conference on Spoken Language Processing (ICSLP-Pittsburgh, PA), 2006
Neuroscience: New Insights for AI?
Poggio, T
In: Web Intelligence Meets Brain Informatics, First WICI International Workshop (WImBI 2006) Bejing, China, December 2006
Object Selectivity of Local Field Potentials and Spikes in the Macaque Inferior Temporal Cortex
Kreiman, G., C.P. Hung, A. Kraskov, R.Q. Quiroga, T. Poggio and J.J. DiCarlo
Neuron, Vol. 49, 433-445, 2006
Optimal Rates for Regularization Operators in Learning Theory
Caponnetto, A
CBCL Paper #264/AI Technical Report #062, Massachusetts Institute of Technology, Cambridge, MA, September, 2006
Perception Strategies in Hierarchical Vision Systems
Wolf, L., S. Bileschi and E. Meyers
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006
Sequence Motifs Predictive of Tissue-specific Skipping
Soni, Neha (S.M. Thesis, EECS, MIT, February 2006):
Stability of K-means Clustering
Rakhlin, A. and A. Caponnetto
In: Neural Information Processing Systems Conference, 2006
Stability Properties of Empirical Risk Minimization over Donsker Classes
Caponnetto, A. and A. Rakhlin
Journal of Machine Learning Research, Vol. 7, 2565-2583, 2006
StreetScenes: Towards Scene Understanding in Still Images
Bileschi, Stanley (Ph.D. Thesis, EECS, MIT, May 2006):
2005
“A Learning Market-Maker in the Glosten-Milgrom Model”
Das, S
Quantitative Finance, Vol. 5, No. 2, 169-180, April 2005
A Combinatorial Code for Splicing Silencing: UAGG and GGGG Motifs
Han, K., G. Yeo, P. An, C.B. Burge and P.J. Grabowski
PLoS Biology, Vol. 3, Issue 5, e158, 0001-0018, May 2005
A Contour-Based Moving Object Detection and Tracking
Yokoyama, M. and T. Poggio
In: Proceedings of Second Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (in conjunction with ICCV 2005), Beijing, China, October 15-16, 2005
A Note on the Role of Squared Loss in Regression
Caponnetto, A
CBCL Paper, Massachusetts Institute of Technology, Cambridge, MA, June 2005
A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex
Serre, T., M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman and T. Poggio
CBCL Paper #259/AI Memo #2005-036, Massachusetts Institute of Technology, Cambridge, MA, October, 2005
An Oncogenic KRAS2 Expression Signature Identified by Cross-species Gene-expression Analysis
Sweet-Cordero, A., S. Mukherjee, A. Subramanian, H. You, J.J. Roix, C. Ladd-Acosta, J. Mesirov, T.R. Golub and T. Jacks
Nature Genetics, 37, 1, 48-55, 2005
Asymptotics of Gaussian Regularized Least-Squares
Lippert, J.R. and R. Rifkin
CBCL Paper #257/AI Memo #2005-030, Massachusetts Institute of Technology, Cambridge, MA, October 2005
Boosting a Biologically Inspired Local Descriptor for Geometry-free Face and Full Multi-view 3D Object Recognition
Yokono, J.J. and T. Poggio
CBCL Paper #254/AI Memo #2005-023, Massachusetts Institute of Technology, Cambridge, MA, July 2005
Combining Variable Selection with Dimensionality Reduction
Wolf, L. and S. Bileschi
CBCL Paper #247/AI Memo #2005-009, Massachusetts Institute of Technology, Cambridge, MA, March 2005
Comparing Visual Features for Morphing Based Recognition
Wu, Jia Jane (S.M. Thesis, EECS, MIT, May 2005):
Empirical Effective Dimension and Optimal Rates for Regularized Least Squares Algorithm
Caponnetto, A., L. Rosasco, E. de Vito and A. Verri
CBCL Paper #252/AI Memo #2005-019, Massachusetts Institute of Technology, Cambridge, MA, May 2005
Error Weighted Classifier Combination for Multi-modal Human Identification
Ivanov, Y., T. Serre and J. Bouvrie
CBCL Paper #258/AI Memo #2005-035, Massachusetts Institute of Technology, Cambridge, MA, December, 2005
Experience-Dependent Sharpening of Visual Shape Selectivity in Inferior Temporal Cortex
Freedman, D.J., M. Riesenhuber, T. Poggio and E.K. Miller
Cerebral Cortex, December 2005
Experiments in Expression Recognition
Skelley, James P. (S.M. Thesis, EECS, MIT, August 2005):
Fast Rates for Regularized Least-squares Algorithm
Caponnetto, A. and E. de Vito
CBCL Paper #248/AI Memo #2005-013, Massachusetts Institute of Technology, Cambridge, MA, April, 2005
Fast Readout of Object Identity from Macaque Inferior Temporal Cortex
Hung, C.P., G. Kreiman, T. Poggio and J.J. DiCarlo
Science, Vol. 310, 863-866, 2005
Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-based Approach
Wolf, L. and A. Shashua
Journal of Machine Learning Research, 6, 1855-1887, 2005
Identification and Analysis of Alternative Splicing Events Conserved in Human and Mouse
Yeo, G., E. Van Nostrand, D. Holste, T. Poggio and C.B. Burge
Proceedings of the National Academy of Sciences (PNAS), 102, 8, 2850-2855, 2005
Invariant Visual Representation by Single Neurons in the Human Brain
Quiroga, R.Q., L. Reddy, G. Kreiman, C. Koch and I. Fried
Nature-Letters, Vol. 435, 1102-1107, June 2005
Learning Features of Intermediate Complexity for the Recognition of Biological Motion
Sigala, R., T. Serre, T. Poggio and M. Giese
In: ICANN 2005, Warsaw, Poland, 241-246, September 2005
Learning to Trade with Insider Information
Das, S
CBCL Paper #255/AI Memo #2005-028, Massachusetts Institute of Technology, Cambridge, MA, October, 2005
Modeling Shape Representation in Visual Cortex Area V4
Cadieu, Charles F. (S.M. Thesis, EECS, MIT, May 2005):
Morphing Spectral Envelopes Using Audio Flow
Ezzat, T., E. Meyers, J. Glass and T. Poggio
In: Proceedings of Interspeech-Eurospeech Conference, Lisbon, Portugal, 2545-2548, September 2005
Multiple Object Response Normalization in Monkey Infero-temporal Cortex
Zoccolan, D., D.D. Cox and J.J. DiCarlo
Journal of Neuroscience, 25(36), 8150-64, 2005
Object Recognition with Features Inspired by Visual Cortex
Serre, T., L. Wolf and T. Poggio
In: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society Press, San Diego, June 2005
Risk Bounds for Regularized Least-squares Algorithm with Operator-valued Kernels
de Vito, E. and A. Caponnetto
CBCL Paper #249/AI Memo #2005-015, Massachusetts Institute of Technology, Cambridge, MA, May, 2005
Robust Boosting for Learning from Few Examples
Wolf, L. and I. Martin
In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005
Robust Learning and Segmentation for Scene Understanding
Martin, Ian S. (S.M. Thesis, EECS, MIT, May 2005):
Some Properties of Empirical Risk Minimization over Donsker Classes
Caponnetto, A. and A. Rakhlin
CBCL Paper #250/AI Memo #2005-018, Massachusetts Institute of Technology, Cambridge, MA, May 2005
Stability Results in Learning Theory
Rakhlin, A., S. Mukherjee and T. Poggio
Analysis and Applications, Vol. 3, No. 4, 397-417, 2005
Towards the Prevention of Dyslexia
Geiger, G. and D. Amara
CBCL Paper #256/AI Memo #2005-029, Massachusetts Institute of Technology, Cambridge, MA, October 2005
Two-Sided Bandits and the Dating Market
Das, S. and E. Kamenica
In: Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, UK, 947-952, August 2005
Ultra-fast Object Recognition from Few Spikes
Hung, C., G. Kreiman, T. Poggio and J. DiCarlo
CBCL Paper #253, Massachusetts Institute of Technology, Cambridge, MA, July 2005
Using Computational Models to Study Texture Representations in the Human Visual System
Balas, B
CBCL Paper #244/AI Memo #2005-002, Massachusetts Institute of Technology, Cambridge, MA, February, 2005
2004
“Francis H. C. Crick: Memories of a friend of Francis and Odile”
Poggio, T
in the Crick Memorial Book: A Memorial Exhibition, curator Becky Cohen, The Salk Institute, 2004
“Object Recognition and Random Image Structure Evolution,”
Sadr, J. and P. Sinha
Cognitive Science, Vol. 28, 259-287, 2004
A Gene Atlas of the Mouse and Human Protein-Encoding Transcriptomes
Su, A.I., T. Wiltshire, S. Batalov, H. Lapp, K.A. Ching, D. Block, J. Zhang, R. Soden, M. Hayakawa, G. Kreiman, M.P. Cooke, J.R. Walker and J.B. Hogenesc
Proceedings of the National Academy of Sciences, USA 101, 6062-6067, 2004
A General Mechanisms for Tuning: Gain Control Circuits and Synapses Underlie Tuning of Cortical Neurons
Kouh, M. and T. Poggio
CBCL Paper #245/AI Memo #2004-031, Massachusetts Institute of Technology, Cambridge, MA, December 2004
A New Biologically Motivated Framework for Robust Object Recognition
Serre, T., L. Wolf and T. Poggio
CBCL Paper #243/AI Memo #2004-026, Massachusetts Institute of Technology, Cambridge, MA, November, 2004
Automatic Facial Expression Analysis and Emotional Classification
Fischer, Robert (S.M. Thesis, Math & Natural Sciences, MIT/Univ. of Applied Science Darmstadt, Germany, October 2004):
Component-based Car Detection in Street Scene Images
Leung, Brian (S.M. Thesis, EECS, MIT, May 2004):
Components for Face Recognition
Heisele, B. and T. Koshizen
In: Proceedings of the 6th International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, 153-158, 2004
Consciousness and Neurosurgery
Crick, F., C. Koch, G. Kreiman and I. Fried
Neurosurgery, 55, 272-282, 2004
Contextually Evoked Object-specific Responses in Human Visual Cortex
Cox, D., E. Meyers and P. Sinha
Science, Vol. 303, No. 5667, 115-117, 2004
Direction Estimation of Pedestrian from Multiple Still Images
Shimizu, H. and T. Poggio
In: IEEE Intelligent Vehicles Symposium 2004, Parma, Italy, June 14-17, 2004
Evaluation of Sets of Oriented and Non-oriented Receptive Fields as Local Descriptors
Yokono, J.J. and T. Poggio
CBCL Paper #237/AI Memo #2004-007, Massachusetts Institute of Technology, Cambridge, MA, March, 2004
Face Processing in Humans is Compatible with A Simple Shape-based Model of Vision
Riesenhuber, M., I. Jarudi, S. Gilad and P. Sinha
Proc. R. Soc. Lond. B (Suppl.), DOI 10.1098/rsbl.2004.0216, 04BL0061.S1-04BL0061.S3, 2004
Face Processing in Humans is Compatible with a Simple-Shape-based Model of Vision
Riesenhuber, M., I. Jarudi, S. Gilad and P. Sinha
CBCL Paper #236/AI Memo #2004-006, Massachusetts Institute of Technology, Cambridge, MA, March, 2004
Face Processing in Video
Weyrauch, B., J. Huang, B. Heisele and V. Blanz
In: Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), IEEE Computer Society Press, Washington, DC, 2004, in press
Francis Harry Compton Crick
Poggio, T.P. and M. Poggio
Physics Today, 80-81, November 2004
General Conditions for Predictivity in Learning Theory
Poggio, T., R. Rifkin, S. Mukherjee and P. Niyogi
Nature, Vol. 428, 419-422, 2004
Generalization in Vision and Motor Control
Poggio, T. and E. Bizzi
Nature, Vol. 431, 768-774, 2004
Identification of Sparsely Distributed Clusters of Cis-Regulatory Elements in Sets of Co-expressed Genes
Kreiman, G
Nucleic Acids Research, 32, 2889-2900, 2004
In Defense of One-vs-All Classification
Rifkin, R. and A. Klautau
Journal of Machine Learning Research, Vol. 5, 101-141, 2004
Intracellular Measurements of Spatial Integration and the MAX Operation in Complex Cells of the Cat Primary Visual Cortex
Lampl, I., D. Ferster, T. Poggio and M. Riesenhuber
Journal of Neurophysiology, 92, 2704-2713, 2004
Multi-Source Contingency Clustering
Bouvrie, Jacob V. (S.M. Thesis, EECS, MIT, June 2004):
Neural Coding: Computational and Biophysical Perspectives
Kreiman, G
Physics of Life Reviews, 2, 71-102, 2004
On Stability and Concentration of Measure
Rakhlin, A., S. Mukherjee, and T. Poggio
CBCL Paper, Massachusetts Institute of Technology, Cambridge, MA, June 2004
On the Difficulty of Feature-based Attentional Modulations in Visual Object Recognition: A Modeling Study
Schneider, R. and M. Riesenhuber
CBCL Paper #235/AI Memo #2004-004, Massachusetts Institute of Technology, Cambridge, MA, February, 2004
Oriented Filters for Object Recognition: An Empirical Study
Yokono, J.J. and T. Poggio
In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR2004, Seoul, Korea), 755-760, 2004
Q & A – Discussion
Poggio, T
Current Biology, Vol. 14, Issue 23, R985-R986, December 2004
Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model, and Implications for Invariant Object Recognition in Cortex
Serre, T. and M. Riesenhuber
CBCL Paper #239/AI Memo #2004-017, Massachusetts Institute of Technology, Cambridge, MA, August, 2004
Regularization through Feature Knock Out
Wolf, L. and I. Martin
CBCL Paper #242/AI Memo #2004-025, Massachusetts Institute of Technology, Cambridge, MA, November, 2004
Responses of Single Neurons in the Human Brain during Flash Suppression
Kreiman, G., I. Fried and C. Koch
In: Binocular Rivalry and Perceptual Ambiguity, (Eds.) R. Blake and D. Alais, MIT Press, Cambridge, MA, Chapter 12, 2005, to appear
Risk Bounds for Mixture Density Estimation
Rakhlin, A., D. Panchencko and S. Mukherjee
CBCL Paper #233/AI Memo #2004-001, Massachusetts Institute of Technology, Cambridge, MA, January, 2004
Rotation Invariant Object Recognition from One Training Example
Yokono, J.J. and T. Poggio
CBCL Paper #238/AI Memo #2004-010, Massachusetts Institute of Technology, Cambridge, MA, April, 2004
Selecting Relevant Genes with a Spectral Approach
Wolf, L., A. Shashua and S. Mukherjee
CBCL Paper #234/AI Memo #2004-002, Massachusetts Institute of Technology, Cambridge, MA, January, 2004
Selectivity of Local Field Potentials in Macaque Inferior Temporal Cortex
Kreiman, G., C. Hung, T. Poggio and J. DiCarlo
CBCL Paper #240/AI Memo #2004-020, Massachusetts Institute of Technology, Cambridge, MA, September, 2004
Shape Representation in V4: Investigating Position-specific Tuning for Foundary Conformation with the Standard Model of Object Recognition
Cadieu, C., M. Kouh, M. Riesenhuber and T. Poggio
CBCL Paper #241/AI Memo #2004-024, Massachusetts Institute of Technology, Cambridge, MA, November, 2004
Stereovision-based Vehicle Classification Using Support Vector Machines
Paysan, Pascal (S.M. Thesis, Computer Science, Fachochschule Esslingen, February 2004):
Supervised Information Retrieval for Text and Images
Kyriakides, Alexandros (S.M. Thesis, EECS, MIT, February 2004):
Systematic Identification and Analysis of Exonic Splicing Silencers
Wang, Z., M.E. Rolish, G. Yeo, V. Tung, M. Mawson and C.B. Burge
Cell, 119, 831-845, 2004
Trainable Videoreaslistic Speech Animation
Ezzat, T., G. Geiger and T. Poggio
In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR2004, Seoul, Korea), 57-64, 2004
Using Component Features for Face Recognition
Ivanov, I., B. Heisele and T. Serre
In: Proceedings of the 6th International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, 421-426, 2004
Variation in Alternative Splicing across Human Tissues
Yeo, G., D. Holste, G. Kreiman and C. Burge
Genome Biology, 5, R74, 2004
Variation in Sequence and Organization of Splicing Regulatory Elements in Vertebrate Genes
Yeo, G., S. Hoon, B. Venkatesh and C. Burge
Proceedings of the National Academy of Sciences (PNAS), 191(44), 15700-15705, 2004
Wider Recognition in Peripheral Vision Common to Different Subtypes of Dyslexia
Lorusso, M.L., A. Facoetti, S. Pesenti, C. Cattaneo, M. Molteni and G. Geiger
Vision Research, 44, 2413-2424, 2004
2003
“The Role of Eyebrows in Face Recognition,”
Sadr, J., I. Jarudi and P. Sinha
Perception, 32, 285-293, 2003
A Biological Model of Object Recognition with Feature Learning
Louie, Jennifer (S.M. Thesis, EECS, MIT, May 2003):
Advances in Component Based Face Detection
Bileschi, S.M. and B. Heisele
In: Proceedings of the 2003 IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2003, Nice France), 149-156, 2003
An Analytical Method for Multi-class Molecular Cancer Classification
Rifkin, R., S. Mukherjee, P. Tamayo, S. Ramaswamy, C.-H. Yeang, M. Angelo, M. Reich, T. Poggio, E.S. Lander, T.R. Golub and J.P. Mesirov
SIAM Reviews, Vol. 45, No. 4, 706-723, 2003
Bayesian Approach to Transcript Estimation from Gene Array Data: The BEAM Technique
Dror, R.O., J.G. Murnick, N.A. Rinaldi, V.D. Marinescu, R.M. Rifkin and R.A. Young
In: Proceedings of the Sixth Annual International Conference on Research in Computational Molecular Biology, Washington, D.C., April 2002, in press
Classifying Microarray Data Using Support Vector Machines
Mukherjee, S
In: A Practical Approach to Microarray Data Analysis, D.P. Berrar, W. Dubitzky and M. Granzow (Eds.), Kluwer Academic Publishers, Boston, MA, Chapter 9, 166-185, 2003
Comparison of Primate Prefrontal and Inferior Temporal Cortices during Visual Categorization
Freedman, D.J., M. Riesenhuber, T. Poggio, and E.K. Miller
Journal of Neuroscience, 23, 5235-5246, 2003
Component-based Face Recognition with 3D Morphable Models
Huang, J. (S.M. Thesis, EECS, MIT, January 2003):
Component-based Recognition of Objects in an Office Environment
Morgenstern, C. and B. Heisele
CBCL Paper #232/AI Memo #2003-024, Massachusetts Institute of Technology, Cambridge, MA, November 2003
Direction Estimation of Pedestrain from Images
Shimizu, H. and T. Poggio
CBCL Paper #230/AI Memo #2003-020, Massachusetts Institute of Technology, Cambridge, MA, August 2003
Dissociated Dipoles: Image Representation Via Non-localComparisons
Balas, B.J. and P. Sinha
CBCL Paper #229/AI Memo #2003-018, Massachusetts Institute of Technology, Cambridge, MA, August 2003
Estimating Dataset Size Requirements for Classifying DNA Microarray Data
Mukherjee, S., P. Tamayo, S. Rogers, R. Rifkin, A. Engle, C. Campbell, T.R. Golub and J.P. Mesirov
Journal of Computational Biology, 2003, in press
Face Recognition: Component-based versus Global Approaches
Heisele, B., P. Ho, J. Wu and T. Poggio
Computer Vision and Image Understanding, Vol. 91, No. 1/2, 6-21, 2003
Face Representation in Cortex: Studies Using a Simple and Not So Special Model
Rosen, Ezra (S.M. Thesis, EECS, MIT, May 2003):
Full-body Person Recognition System
Nakajima, C., M. Pontil, B. Heisele and T. Poggio
Pattern Recognition, 36, 1997-2006, 2003
Hierarchical Classification and Feature Reduction for Fast Face Detection with Support Vector
Heisele, B., T. Serre, S. Prentice and T. Poggio
Pattern Recognition, 36, 2007-2017, 2003
How Visual Cortex Recognizes Objects: The Tale of the Standard Model
Riesenhuber, M. and T. Poggio
In: The Visual Neurosciences, (Eds. L.M. Chalupa and J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653, 2003
Intelligent Market-Making in Artificial Financial Markets
Das, S
CBCL Paper #226/AI Technical Report #2003-005, Massachusetts Institute of Technology, Cambridge, MA, May 2003
Intelligent Market-Making in Artificial Markets
Das, Sanmay (S.M. Thesis, EECS, MIT, June 2003):
Investigating Shape Representation in Area V4 with HMAX: Orientation and Grating Selectivities
Kouh, M. and M. Riesenhuber
CBCL Paper #231/AIM #2003-021, Massachusetts Institute of Technology, Cambridge, MA, September 2003
Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals
Yeo, G. and C. Burge
In: Proceedings of the Seventh Annual International Conference on Research in Computational Molecular Biology, Berlin, Germany, April 10-13, 2003
Multi-Source Human Identification
Kim, Brian (S.M. Thesis, EECS, MIT, June 2003):
Neural Mechanisms for the Recognition of Biological Movements
Giese, M. and T. Poggio
Nature Neuroscience Review, Vol. 4, 179-192, March 2003
Perceptual Evaluation of Video-realistic Speech
Geiger, G., T. Ezzat and T. Poggio
CBCL Paper #224/AI Memo #2003-003, Massachusetts Institute of Technology, Cambridge, MA, February 2003
Permutation Tests for Classification
Mukherjee, S., P. Golland and D. Panchenko
AI Memo #2003-019, Massachusetts Institute of Technology, Cambridge, MA, August 2003
Reanimating Faces in Images and Video
Blanz, V., C. Basso, T. Poggio and T. Vetter
In: Proceedings of Eurographics, (Eds. P. Brunet and D. Fellner), Granada, Spain, Vol. 22, No. 3, 2003
Regularized Least Squares Classification
Rifkin, R., G. Yeo and T. Poggio
In: Advances in Learning Theory: Methods, Model and Applications, NATO Science Series III: Computer and Systems Sciences, VIOS Press, Amsterdam, (Eds.) Suykens, Horvath, Basu, Micchelli and Vandewalle, Vol. 190, Chapter 7, 131-154, 2003
Relative Contributions of Internal and External Features to Face Recognition
Jarudi, I.N. and P. Sinha
CBCL Paper #225/AI Memo #2003-004, Massachusetts Institute of Technology, Cambridge, MA, March 2003
The Mathematics of Learning: Dealing with Data
Poggio, T. and S. Smale
Notices of the American Mathematical Society (AMS), Vol. 50, No. 5, 537-544, 2003. (See journal issue at AMS Notices.)
Visual Object Recognition with Supervised Learning
Heisele, B
IEEE Intelligent Systems – AI’s Second Century, 38-42, May-June 2003
2002
“Biophysiologically Plausible Implementations of the Maximum Operation,”
Yu, A.J., M.A. Giese and T. Poggio
Neural Computation, Vol. 14, No. 12, 2857-2881, 2002
“Enhanced Perceptual Sensitivity for Anger among Physically Abused Children,”
Pollak, S. and P. Sinha
Developmental Psychology, Vol. 38, No. 5, 784-791, 2002
“Identifying Perceptually Significant Features for Recognizing Faces.”
Sinha, P
In: Proceedings of the SPIE Electronic Imaging 2002 Symposium – Electronic Imaging Systems and Image Processing Methods, Human Vision and Electronics Imaging VII, January 21-24, 2002
“Role of Color in Face Recognition,”
Yip, A. and P. Sinha
Perception, Vol. 31, 995-1003, 2002
A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition
Schneider, R. and M. Riesenhuber
CBCL Paper #218/AI Memo #2002-011, Massachusetts Institute of Technology, Cambridge, MA, August 2002
Advances in Component-based Face Detection
Bileschi, S.M. and B. Heisele
In: Proceedings of Pattern Recognition with Support Vector Machines, First International Workshop, SVM 2002, Niagara Falls, Canada, S.-W. Lee and A. Verri (eds.), Lecture Notes in Computer Science, Springer LNCS 2388, 135-143, 2002
Attentional Selection for Object Recognition \96 A Gentle Way
Walther, D., L. Itti, M. Riesenhuber, T. Poggio, and C. Koch
In: Biologically Motivated Computer Vision, Second International Workshop (BMCV 2002), T\FCbingen, Germany, 472-479, 2002
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Poggio, T., S. Mukherjee, R. Rifkin, A. Rakhlin and A. Verri
In: Uncertainty in Geometric Computations, J. Winkler and M. Niranjan (eds.), Kluwer Academic Publishers, 131-141, 2002
Bagging Regularizes
Poggio, T., R. Rifkin, S. Mukherjee and A. Rakhlin
CBCL Paper #214/AI Memo #2002-003, Massachusetts Institute of Technology, Cambridge, MA, February 2002
Biologically Plausible Neural Model for the Recognition of Biological Motion and Actions
Giese, M.A. and T. Poggio
CBCL Paper #219/AI Memo #2002-012, Massachusetts Institute of Technology, Cambridge, MA, August 2002
Categorization by Learning and Combining Object Parts
Heisele, B., T. Serre, M. Pontil, T. Vetter and T. Poggio
In: Advances in Neural Information Processing Systems 14, Vancouver, Canada, Vol. 2, 1239-1245, 2002
Categorization in IT and PFC: Model and Experiments
Knoblich, U., D. Freedman and M. Riesenhuber
CBCL Paper #216/AI Memo #2002-007, Massachusetts Institute of Technology, Cambridge, MA, April 2002
Everything Old Is New Again: A Fresh Look at Historical Approaches in Machine Learning
Rifkin, R. (Ph.D. Thesis, EECS & OR, MIT, September, 2002):
Exact Solution of the Nonlinear Dynamics of Recurrent Neural Mechanisms for Direction Selectivity
Giese, M.A. and X. Xie
CBCL Paper #220/AI Memo #2002-013, Massachusetts Institute of Technology, Cambridge, MA, August 2002
Extra-label Information: Experiments with View-based Classification
Rakhlin, A., G. Yeo and T. Poggio
In: Proceedings of the Sixth International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES’2002), Podere d’Ombriano, Crema, Italy, September 16-18, 2002
Face Recognition Using Component-Based SVM Classification and Morphable Models
Huang, J., V. Blanz and B. Heisele
In: Proceedings of Pattern Recognition with Support Vector Machines, First International Workshop, SVM 2002, Niagara Falls, Canada, S.-W. Lee and A. Verri (eds.), Lecture Notes in Computer Science, Springer 2388, 334-341, 2002
Handheld Face Identification Technology in a Pervasive Computing Environment
Weinstein, E., P. Ho, B. Heisele, T. Poggio, K. Steele and A. Agarwal
In: Proceedings of Pervasive 2002, Zurich, Switzerland, August 26-28, 48-54, 2002
Image Representations and Feature Selection for Multimedia Database Search
Evgeniou, T., M. Pontil, C. Papageorgiou and T. Poggio
In: IEEE Transactions in Knowledge and Data Engineering, May 2002, to appear
Learning and Vision Machines
Heisele, B., A. Verri and T. Poggio
In: Proceedings of the IEEE, Visual Perception: Technology and Tools, Vol. 90, No. 7, 1164-1177, 2002
Learning from Partially Labeled Data
Szummer, M. (Ph.D. Thesis, EECS, MIT, September 2002):
Model Selection in Summary Evaluation
Perez-Breva, L. and O. Yoshimi
CBCL Paper #222/AI Memo #2002-023, Massachusetts Institute of Technology, Cambridge, MA, December 2002
Modeling Stock Order Flows and Learning Market-Making from Data
Kim, A.J. and Shelton, C.R
CBCL Paper #217/AI Memo #2002-009, Massachusetts Institute of Technology, Cambridge, MA, June 2002
Neural Mechanisms of Object Recognition
Riesenhuber, M. and T. Poggio
Current Opinion in Neurobiology, 12, 162-168, 2002
Object Recognition with Partially Labeled Examples
Crane, A. (S.M. Thesis, EECS, MIT, September 2002):
On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision
Serre, T., M. Riesenhuber, J. Louie, and T. Poggio
In: Biologically Motivated Computer Vision, Second International Workshop (BMCV 2002), T\FCbingen, Germany, 387-397, 2002
Perceptual Learning
Fahle, M. and T. Poggio (Eds.)
MIT Press, Cambridge, MA, 2002
Prediction of Central Nervous System Embryonal Tumour Outcome Based on Gene Expression
Pomeroy, S.L., P. Tamayo, M. Gaasenbeek, L.M. Sturia, M. Angelo, M.E. McLaughlin, J.Y.H. Kim, L.C. Goumnerova, P.M. Black, C. Lau, J.C. Allen, D. Zagzag, M.M. Olson, T. Curran, C. Wetmore, J.A. Biegel, T. Poggio, S. Mukherjee, R. Rifkin, A. Califano, G. Stolovitzky, D.N. Louis, J.P. Mesirov, E.S. Lander and T.R. Golub
Nature (Letters to Nature), Vol. 415, 436-442, 2002
Regression and Classification with Regularization
Mukherjee, S., R. Rifkin and T. Poggio
In: Lectures Notes in Statistics: Nonlinear Estimation and Classification, Proceedings from MSRI Workshop, D.D. Denison, M.H. Hansen, C.C. Holmes, B. Mallick and B. Yu (eds.), Springer-Verlag, 171, 107-124, 2002
Single Neuron Correlates of Subjective Vision in the Human Medial Temporal Lobe
Kreiman, G., I. Fried, and C. Koch
Proceedings of the National Academy of Sciences, USA 99, 8378-8383, 2002
Statistical Learning: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization
Mukherjee, S., P. Niyogi, T. Poggio and R. Rifkin
CBCL Paper #223, Massachusetts Institute of Technology, Cambridge, MA, December 2002 [January 2004 revision]
Stimulus Simplification and Object Representation: A Modeling Study
Knoblich, U. and M. Riesenhuber
CBCL Paper #215/AI Memo #2002-004, Massachusetts Institute of Technology, Cambridge, MA, March 2002
Towards Man-machine Interfaces: Combining Top-down Constraints with Bottom-up Learning in Facial Analysis
Kumar, V.P
CBCL Paper #221/AI Technical Report #2002-008, Massachusetts Institute of Technology, Cambridge, MA, August 2002
Towards Trainable Man-machine Interfaces: Combining Top-down Constraints with Bottom-up Learning in Facial Analysis
Kumar, V. (Ph.D. Thesis, BCS, MIT, June 2002):
Trainable Videorealistic Speech Animation
Ezzat, T., G. Geiger and T. Poggio
In: Proceedings of ACM SIGGRAPH 2002, San Antonio, TX, 388-398, 2002
United We Stand: The Role of Head Structure in Face Recognition
Sinha, P. and T. Poggio
Perception, 31/1, 133, 2002
Visual Categorization and the Primate Prefrontal Cortex: Neurophysiology and Behavior
Freedman, D.J., M. Riesenhuber, T. Poggio, and E.K. Miller
Journal of Neurophysiology, 88, 930-942, 2002
Visual Categorization: How the Monkey Brain Does It
Knoblich, U., M. Riesenhuber, D.J. Freedman, E.K. Miller and T. Poggio
In: Biologically Motivated Computer Vision, Second International Workshop (BMCV 2002), T\FCbingen, Germany, 273-281, 2002
2001
“Functional Neuroanatomy of Biological Motion Perception in Humans.”
Vaina, L.M., J. Solomon, S. Chowdry, P. Sinha, J.W. Belliveau, and C.G. Gross
In: Proceedings of the National Academy of Sciences, 98, 11656-11661, 2001
A Note on the Generalization Performance of Kernel Classifiers with Margin
Evgeniou, T. and M. Pontil
In: Proceedings of Algorithmic Learning Theory 2000 Conference, Lecture Notes in Artificial Intelligence, Sydney, Australia (December 11-13, 2000), Vol. 1968, 306-315, 2001
Agent-Based Models of Financial Markets: A Comparison with Experimental Markets
Chan, N.T., B. LeBaron, A.W. Lo and T. Poggio
MIT Sloan Working Paper No. 4195-01, Social Science Research Network Electronic Library, October 2001
An Electronic Market-Maker
Chan, N. and C. Shelton
CBCL Paper #195/AI Memo #2001-005, Massachusetts Institute of Technology, Cambridge, MA, April 2001
An Empirical Comparison of SNoW and SVMs for Face Detection
Alvira, M. and Rifkin, R
CBCL Paper #193/AI Memo #2001-004, Massachusetts Institute of Technology, Cambridge, MA, January 2001
Application of Statistical Learning Theory to DNA Microarray Analysis
Mukherjee, Sayan (Ph.D. Thesis, BCS, MIT, June 2001):
Artificial Markets and Intelligent Agents
Chan, Nicholas (Ph.D. Thesis, EECS, MIT, February 2001):
b
Poggio, T., S. Mukherjee, R. Rifkin, A. Rakhlin and A. Verri
CBCL Paper #198/AI Memo #2001-011, Massachusetts Institute of Technology, Cambridge, MA, July 2001
Balancing Multiple Sources of Reward in Reinforcement Learning
Shelton, C
In: Advances in Neural Information Processing Systems (NIPS), 1082-1088, 2001
Biological Plausible Neural Circuits for Realization of the Maximum Operations
Yu, A., M.A. Giese and T. Poggio
CBCL Paper #207/AI Memot #2001-022, Massachusetts Institute of Technology, Cambridge, MA, September 2001
Bounds on Sample Size for Policy Evaluation in Markov Environments
Peshkin, L. and S. Mukherjee
In: Proceedings of COLT 2001: The Fourteenth Annual Conference on Computational Learning Theory 2001, Amsterdam, Netherlands (July 16-19, 2001), to appear
Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex
Freedman, D.J., M. Riesenhuber, T. Poggio and E.K. Miller
Science, 291, 312-316, 2001
Choosing Multiple Parameters for Support Vector Machines
Chapelle, O., V. Vapnik, O. Bousquet and S. Mukherjee
Machine Learning – Special Issue on Support Vector Machines, 2001
Component-based Face Detection
Heisele, B., T. Serre, M. Pontil and T. Poggio
In: Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), IEEE Computer Society Press, Kauai, Hawaii, Vol. 1, 657-662, December 2001
Contextual Priming for Object Detection
Torralba, A. and P. Sinha
CBCL Paper #205/AI Memo #2001-020, Massachusetts Institute of Technology, Cambridge, MA, September 2001
Detecting Faces in Impoverished Images
Torralba, A. and P. Sinha
CBCL Paper #208/AI Memo #2001-028, Massachusetts Institute of Technology, Cambridge, MA, November 2001
Example-based Object Detection in Images by Components
Mohan, A., C. Papageorgiou and T. Poggio
IEEE (PAMI), Vol. 23, No. 4, 349-361, April 2001
Experimental Markets for Product Concepts
Chan, N., E. Dahan, A. Lo and T. Poggio
CBCL Paper #200/AI Memo #2001-013, Massachusetts Institute of Technology, Cambridge, MA, July 2001
Exploring Object Perception with Random Image Structure Evolution
Sadr, J. and P. Sinha
CBCL Paper #196/AI Memo #2001-006, Massachusetts Institute of Technology, Cambridge, MA, March 2001
Face Recognition with Support Vector Machines: Global Versus Component-based Approach
Heisele, B., P. Ho and T. Poggio
International Conference on Computer Vision (ICCV’01), Vancouver, Canada, Vol. 2, 688-694, 2001
Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images
Heisele, B., T. Serre, S. Mukherjee and T. Poggio
In: Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), IEEE Computer Society Press, Kauai, Hawaii, Vol. 2, 18-24, December 2001
Feature Selection for SVMs
Weston, J., S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio and V. Vapnik
In: Advances in Neural Information Processing Systems (NIPS), 13, 668-674, 2001
Generalization Over Contrast and Mirror Reversal, but Not Figure-ground Reversal, in an “Edge-based” Model of IT Neurons
Riesenhuber, M
CBCL Paper #211/AI Memo #2001-034, Massachusetts Institute of Technology, Cambridge, MA, December 2001
Global Depth Perception from Familiar Scene Structure
Torralba, A. and A. Oliva
CBCL Paper #213/AI Memo #2001-036, Massachusetts Institute of Technology, Cambridge, MA, December 2001
Importance Sampling for Reinforcement Learning with Multiple Objectives
Shelton, C
CBCL Paper #204/AI Technical Report #2001-003, Massachusetts Institute of Technology, Cambridge, MA, August 2001
Improving Multiclass Text Classification with the Support Vector Machine
Rennie, J.D.M. and R. Rifkin
CBCL Paper #210/AI Memo #2001-026, Massachusetts Institute of Technology, Cambridge, MA, October 2001
Incremental and Decremental Support Vector Machine Learning
Cauwenberghs, G. and T. Poggio
In: Advances in Neural Information Processing Systems (NIPS*2000), MIT Press, Vol. 13, 409-415, Cambridge, MA, 2001
Molecular Classification of Multiple Tumor Types
Yeang, C.-H., S. Ramaswamy, P. Tamayo, S. Mukherjee, R.M. Rifkin, M. Angelo, M. Reich, E. Lander, J. Mesirov, and T. Golub
In: Intelligent Systems in Molecular Biology – Bioinformatics Discovery Note, Proceedings of the Ninth International Conference on Intelligent Systems in Molecular Biology, Copenhagen, Denmark, (July 21-25, 2001), Vol. 1, No. 1, S316-S322, 2001
Multiclass Cancer Diagnosis Using Tumor Gene Expression Signatures
Ramaswamy, S., P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J.P. Mesirov, T. Poggio, W. Gerald, M. Loda, E.S. Lander, and T.R. Golub
PNAS, Vol. 98, No. 26, 15149-15154, December 2001
Multiclass Classification of SRBCTs
Yeo, G. and T. Poggio
CBCL Paper #206/AI Memo #2001-018, Massachusetts Institute of Technology, Cambridge, MA, August 2001
Perceiving Illumination Inconsistencies in Scenes
Ostrovsky, Y., P. Cavanagh and P. Sinha
CBCL Paper #209/AI Memo #2001-029, Massachusetts Institute of Technology, Cambridge, MA, November 2001
Perceptually-based Comparison of Image Similarity Metrics
Russell, R. and P. Sinha
CBCL Paper #201/AI Memo #2001-014, Massachusetts Institute of Technology, Cambridge, MA, July 2001
Policy Improvement for POMDPs Using Normalized Importance Sampling
Shelton, C
CBCL Paper #194/AI Memo #2001-002, Massachusetts Institute of Technology, Cambridge, MA, March 2001
Recognizing Indoor Scenes
Torralba, A. and P. Sinha
CBCL Paper #202/AI Memo #2001-015, Massachusetts Institute of Technology, Cambridge, MA, July 2001
Role of Color in Face Recognition
Yip, A. and P. Sinha
CBCL Paper #212/AI Memo #2001-035, Massachusetts Institute of Technology, Cambridge, MA, December 2001
Role of Low-level Mechanisms in Brightness Perception
Sinha, P. and A. Torralba
CBCL Paper #203/AI Memo #2001-017, Massachusetts Institute of Technology, Cambridge, MA, August 2001
Rotation Invariant Real-time Face Detection and Recognition System
Ho, P
CBCL Paper #197/AI Memo #2001-010, Massachusetts Institute of Technology, Cambridge, MA, May 2001
The Audiomomma Music Recommendation System
Alvira, M., J. Paris and Rifkin, R
CBCL Paper #199/AI Memo #2001-012, Massachusetts Institute of Technology, Cambridge, MA, July 2001
2000
“CBF: A New Framework for Object Categorization in Cortex.”
Riesenhuber, M. and T. Poggio
In: Biologically Motivated Computer Vision, Lee, S-W., H.H. Buelthoff and T. Poggio (eds.), First IEEE International Workshop, BMCV 2000, Seoul, Korea, May 2000
“Learning in Brains and Machines,”
Poggio, T. and C. Shelton
Spatial Vision, Vol. 13, No. 2-3, 287-296, 2000
“Second Wave of Network Technologies,”
Poggio, T
The Future of Software, 1, 1, 84-85, Winter 2000/2001
“The Perception of Gaze Direction,”
Sinha, P
Perception, 2000 (to appear)
A Trainable System for Object Detection
Papageorgiou, C. and T. Poggio
International Journal of Computer Vision, 38, 1, 15-33, 2000
Bounds on the Generalization Performance of Kernel Machine Ensembles
Evgeniou, T., L. Perez-Breva, M. Pontil, and T. Poggio
In: Proceedings of Seventeenth International Conference on Machine Learning, Stanford University, June 29-July 2, 2000, (to appear)
Computational Models of Object Recognition in Cortex: A Review
Riesenhuber, M. and T. Poggio
CBCL Paper #190/AI Memo #1695, Massachusetts Institute of Technology, Cambridge, MA, August 2000
Face Detection in Still Gray Images
Heisele, B., T. Poggio and M. Pontil
CBCL Paper #187/AI Memo #1687, Massachusetts Institute of Technology, Cambridge, MA, June 2000
Feature Selection for Face Detection
Serre, T., B. Heisele, S. Mukherjee and T. Poggio
CBCL Paper #192/AI Memo #1697, Massachusetts Institute of Technology, Cambridge, MA, September 2000
How a Part of the Brain Might or Might Not Work: A New Hierarchical Model of Object Recognition
Riesenhuber, M. (Ph.D. Thesis, BCS, MIT, June 2000):
Introduction: Learning and Vision at CBCL
Poggio, T. and A. Verri
International Journal of Computer Vision, 38, 1, 5-7, 2000
Learning with Kernel Machine Architectures
Evgeniou, T. (Ph.D. Thesis, EECS, MIT, June 2000):
Learning-based Approach to Estimation of Morphable Model Parameters
Kumar, V. and T. Poggio
CBCL Paper #191/AI Memo #1696, Massachusetts Institute of Technology, Cambridge, MA, September 2000
Learning-based Approach to Real Time Tracking and Analysis of Faces
Kumar, V. and T. Poggio
In: Proceedings of the Fourth International Conference on Face and Gesture Recognition, Grenoble, France, 91-96, March, 2000
Morphable Models for the Analysis and Synthesis of Complex Motion Pattern
Giese, M.A. and T. Poggio
International Journal of Computer Vision, 38, 1, 59-73, 2000
Morphable Surface Models
Shelton, C
International Journal of Computer Vision, 38, 1, 75-91, 2000
Neural Field Model for the Recognition of Biological Motion Patterns
Giese, M.A
In: Second International ICSC Symposium on Neural Computation, Berlin, Germany, May 23-26, 2000
Neural Model for the Recognition of Biological Motion
Giese, M.A
In: Dynamische Perzeption, G. Baratoff and H. Neumann (eds.), Infix Verlag, Berlin, 105-110, 2000
Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation
Nakajima, C., I. Norihiko, M. Pontil and T. Poggio
In: Proceedings of International Conference on Pattern Recognition, Barcelona, Spain, September 2000
People Recognition and Pose Estimation in Image Sequences.
Nakajima, C., M. Pontil and T. Poggio
In: Proceedings of IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, July 2000
People Recognition in Image Sequences by Supervised Learning
Nakajima, C., M. Pontil, B. Heisele and T. Poggio
CBCL Paper #188/AI Memo #1688, Massachusetts Institute of Technology, Cambridge, MA, June 2000
Quantification and Classification of Locomotion Patterns by Spatio-temporal Morphable Models
Giese, M.A. and T. Poggio
In: Third IEEE Workshop on Visual Surveillance, Dublin, Ireland, July 1, 2000
Regularization Networks and Support Vector Machines
Evgeniou, T., M. Pontil and T. Poggio
Advances in Computational Mathematics, 13, 1, 1-50, 2000
Statistical Learning Theory: A Primer
Evgeniou, T., M. Pontil and T. Poggio
International Journal of Computer Vision, 38, 1, 9-13, 2000
The Individual is Nothing, the Class Everything: Psychophysics and Modeling of Recognition in Object Classes
Riesenhuber, M. and T. Poggio
CBCL Paper #185/AI Memo #1682, Massachusetts Institute of Technology, Cambridge, MA, April 2000
1999
“An Introduction to Variational Methods for Graphical Models,”
Jordan, M. I., Z. Ghahramani, T.S. Jaakkola and L.K. Saul
Machine Learning, in press
“Computational Motor Control.”
Jordan, M. I., and D. M. Wolpert
In: The Cognitive Neurosciences, 2nd edition, M. Gazzaniga (Ed.), Cambridge, MA, MIT Press, 1999
“Imparare a Vedere.”
Poggio, T
In: Enciclopedia Italiana Treccani, III, 675-685, 1999
“Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixture of Simpler Ones,”
Saul, L. K., and M.I. Jordan
Machine Learning, in press
“Output Units of Motor Behavior: An Experimental and Modeling Study,”
Loeb, E., S. Giszter, P. Saltiel, F.A. Mussa-Ivaldi and E. Bizzi
J. Cognitive Neuroscience, 1999, in press
“Predicting the Visual World: Silence is Golden,”
Koch, C. and T. Poggio
Nature Neuroscience, Vol. 2, No. 1, 9-10, January 1999
“Quick Work but Variable Quality,”
Giese, M.A
Nature, 401, 13, 1999
“The Construction of Movement by the Spinal Cord,”
Tresch, M.C., P. Saltiel and E. Bizzi
Nature Neuroscience, 2:162-167, 1999
“Variational Methods and the QMR-DT Database.”
Jaakkola, T., and M.I. Jordan
In: Neural Networks and Machine Learning, C. M. Bishop (Ed.), Berlin: Springer-Verlag, 1999
“Variational Probabilistic Inference and the QMR-DT Network,”
Jaakkola, T. and M.I. Jordan
Journal of Artificial Intelligence Research, 10, 291-322, 1999
A Note on Object Class Representation and Categorical Perception
Riesenhuber, M. and T. Poggio
CBCL Paper #183/AI Memo #1679, Massachusetts Institute of Technology, Cambridge, MA, December 1999
A Note on Support Vector Machines Degeneracy
Rifkin, R., M. Pontil and A. Verri
CBCL Paper #177/AI Memo #1661, Massachusetts Institute of Technology, Cambridge, MA, June 1999
A Pattern Classification Approach to Dynamical Object Detection
Papageorgiou, C. and T. Poggio
In: Proceedings of International Conference on Computer Vision (ICCV’99), Corfu, Greece, 1223-1228, September 1999
A Trainable Object Detection System: Car Detection in Static Images
Papageorgiou, C. and T. Poggio
CBCL Paper #180/AI Memo #1673, Massachusetts Institute of Technology, Cambridge, MA, April 1999
A Trainable System for Object Detection in Images and Video Sequences
Papageorgiou, Constantine (Ph.D. Thesis, EECS, MIT, December 1999):
A Unified Framework for Regularization Networks and Support Vector Machines
Evgeniou, T., M. Pontil, and T. Poggio
CBCL Paper #171/AI Memo #1654, Massachusetts Institute of Technology, Cambridge, MA, March 1999
An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High Dimensional Sparse Data
Meila, M
CBCL Paper #169/AI Memo #1652, Massachusetts Institute of Technology, Cambridge, MA, 1999
Are Cortical Models Really Bound by the ‘Binding Problem’?
Riesenhuber, M. and T. Poggio
Neuron 24, 87-93, 1999
Dynamic Neural Field Theory of Motion Perception
Giese, M.A
Kluwer Academic Publishers, Dordrecht, Netherlands, 1999
Evidence for Multi-functional Interactions in Early Visual Motion Processing
Giese, M.A
Trends in Neurosciences, Vol. 22, No. 7, 287-290, 1999
GEM: A Global Electronic Market System
Rachlevsky-Reich, B., I. Ben-Shaul, N. Tung Chan, A. Lo and T. Poggio
Information Systems, Vol. 24, No. 6, p. 495-518, 1999
Generalization Bounds for Function Approximation from Scattered Noisy Data
Niyogi, P. and F. Girosi
Advances in Computational Mathematics, Vol. 10, 51-80, 1999
Hierarchical Models of Object Recognition in Cortex
Riesenhuber, M. and T. Poggio
Nature Neuroscience, 2, 1019-1025, 1999
Learning in Graphical Models
Jordan, M. I. (Ed.)
Cambridge, MA, MIT Press, 1999
Learning-based Approach to Real-time Tracking and Analysis of Faces
Kumar, V. and T. Poggio
CBCL Paper #179/AI Memo #1672, Massachusetts Institute of Technology, Cambridge, MA, September 1999
Machine Learning, Machine Vision and the Brain
Poggio, T. and C. Shelton
AI Magazine, Vol. 20, No. 3, 37-55, 1999
Multivariate Density Estimation: An SVM Approach
Mukherjee, S. and V. Vapnik
CBCL Paper #170/AI Memo #1653, Massachusetts Institute of Technology, Cambridge, MA, April 1999
Object Detection in Images by Components
Mohan, A
CBCL Paper #178/AI Memo #1664, Massachusetts Institute of Technology, Cambridge, MA, June 1999
On the V Gamma Dimension for Regression in Reproducing Kernel Hilbert Spaces
Evgeniou, T. and M. Pontil
CBCL Paper #172/AI Memo #1656, Massachusetts Institute of Technology, Cambridge, MA, May 1999
Sparse Correlation Kernel Reconstruction
Papageorgiou, C., F. Girosi and T. Poggio
In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Phoenix, AZ, 1633-1636, March 1999
Support Vector Machine Classification of Microarray Data
Mukherjee, S., P. Tamayo, J.P. Mesirov, D. Slonim, A. Verri and T. Poggio
CBCL Paper #182/AI Memo #1677, Massachusetts Institute of Technology, Cambridge, MA, December 1999
Synthesis and Recognition of Biological Motion Patterns Based on Linear Superposition of Prototypical Motion Sequences
Giese, M.A., and T. Poggio
In: Proceedings of the IEEE Workshop on Multi-View Modeling and Analysis of Visual Scene, Fort Collins, CO, 73-80, 1999
Trainable Pedestrian Detection
Papageorgiou, C. and T. Poggio
International Conference on Image Processing (ICIP’99), Kobe, Japan, October 1999
Visual Speech Synthesis by Morphing Visemes
Ezzat, T. and T. Poggio
CBCL Paper #173/AI Memo #1658, Massachusetts Institute of Technology, Cambridge, MA, May 1999
1998
“A Mean field Learning Algorithm for Unsupervised Neural Networks.”
Saul, L. K., and M. Jordan
In: Learning in Graphical Models, M. I. Jordan (Ed.), Cambridge: MIT Press, 1998
“A Perturbation Scheme for Spherical Arrangements with Application to Molecular Modeling,”
Halperin, D. and Shelton, C.R
Computational Geometry: Theory and Applications, Vol. 10, No. 4, 273-288, 1998
“Adaptation in Speech Motor Control.”
Houde, J. and M.I. Jordan
In: Advances in Neural Information Processing Systems 10, Jordan, M. I., Kearns, M. J. and Solla, S. A. (Eds.), Cambridge MA: MIT Press, 1998
“Adaptation in Speech Production,”
Houde, J. and M.I. Jordan
Science, 279, 1213-1216, 1998
“An Introduction to Variational Methods for Graphical Models.”
Jordan, M. I., Z. Ghahramani, T.S. Jaakkola and L.K. Saul
In: Learning in Graphical Models, M. I. Jordan (Ed.), Cambridge, MIT Press, 1998
“Dynamic Neural Field Model Links Neural and Computational Theories of Motion Perception.”
Giese, M.A
In: Workshop Dynamische Perzeption, Infix Verlag, H. Ritter and S. Posch (eds.), St. Augustin, Germany, 1998
“Example-Based Learning for View-Based Human Face Detection,”
Sung, K.K. and T. Poggio
IEEE PAMI, Vol. 20, No. 1, 39-51, 1998
“Low Dimensionality of Supraspinally Induced Force Fields,”
D’Avella, A. and E. Bizzi
Proc. of the National Academy of Science, 95: 7711-7714, 1998
“Modular Organization of Motor Behavior,”
Bizzi, E., P. Saltiel and M.C. Tresch
Zeitschrift für Naturforschung, 53c: 510-517, 1998
“Sex Classification is Better with Three-Dimensional Head Structure than with Texture,”
O’Toole, A.J., T. Vetter, N.F. Toje and H.H. Bülthoff
Perception, in press
“Smoothness Maximization along a Predefined Path Accurately Predicts the Speed Profiles of Complex Arm Movements,”
Todorov, E. and M. Jordan
Journal of Neurophysiology, 80, 696-714, 1998
“Spinal Cord Modular Organization and Rhythm Generation: An NMDA Iontophoretic Study in the Frog,”
Saltiel, P., M.C. Tresch and E. Bizzi
J. Neurophysiol., 80: 2323-2339, 1998
“The Role Inertial Sensitivity in Motor Planning.”
Sabes, P., M.I. Jordan and D.M. Wolpert
Journal of Neuroscience, 18, 5948-5959, 1998
“VLSI Hardware for Example-based Learning.”
Yang, W. and A. Lipman
In: Proceedings of 1998 Image Understanding Workshop, November 1998
A General Framework for Object Detection
Papageorgiou, C., M. Oren, and T. Poggio
In: Proceedings of the Sixth International Conference on Computer Vision (ICCV’98), Bombay, India, 555-562, January 1998
A Sparse Representation for Function Approximation
Poggio, T. and F. Girosi
Neural Computation, Vol. 10, No. 6, 1445-14454, 1998
A Trainable Pedestrian Detection System
Papageorgiou, C., T. Evgeniou, and T. Poggio
In: Proceedings of Intelligent Vehicles, Stuttgart, Germany, 241-246, 1998
Applications of Artificial Neural Networks to Image Processing
Chellappa, R., K. Fukushima, A.K. Katsaggelos, S-Y. Kung,\A0 Y. LeCun, N.M. Nasrabadi, T. Poggio.\A0
Guest Editorial for em>IEEE Transactions on Image Processing: Special Issue on Applications of Artificial Neural Networks to Image Processing, Volume 7, Number 8, August 1998
Breaking Rotational Symmetry in a Self-Organizing Map-Model for Orientation Map Development
Riesenhuber, M., H.U. Bauer, D. Brockmann, and T. Geisel
Neural Computation, 10, 717-730, 1998
From Regression to Classification in Support Vector Machines
Pontil, M., R. Rifkin and T. Evgeniou
CBCL Paper #166, AI Memo #1649, Massachusetts Institute of Technology, Cambridge, MA, November 1998
Hierarchical Morphable Models
Jones, M. and T. Poggio
In: Proceedings of Computer Vision and Pattern Recognition, Santa Barbara, 820-826, June 23-25, 1998
Incorporating Prior Information in Machine Learning by Creating Virtual Examples
Niyogi, P., T. Poggio, and F. Girosi
In: IEEE Proceedings on Intelligent Signal Processing, Vol. 86, No 11, 2196-2209, 1998
Information Dissemination and Aggregation in Asset Markets with Simple Intelligent Traders
Chan, N., B. LeBaron, A. Lo and T. Poggio
CBCL Paper #164/AI Memo #1646, Massachusetts Institute of Technology, Cambridge, MA, September 1998
Just One View: Invariances in Inferotemporal Cell Tuning
Riesenhuber, M. and T. Poggio
In: Advances in Neural Information Processing Systems, MIT Press, 10, 215-221, 1998
MikeTalk: A Talking Facial Display Based on Morphing Visemes
Ezzat, T. and T. Poggio
In: Proceedings of the Computer Animation Conference, Philadelphia, PA, 96-102, June 1998
Mixed Memory Markov Models for Time Series Analysis
Papageorgiou, C
In: Proceedings of Computational Intelligence for Financial Engineering, New York, 165-170, March 1998
Modeling Invariances in Inferotemporal Cell Tuning
Riesenhuber, M. and T. Poggio
CBCL Paper #160/AI Memo #1629, Massachusetts Institute of Technology, Cambridge, MA, March 1998
Multidimensional Morphable Models
Jones, M. and T. Poggio
In: Proceedings of the Sixth International Conference on Computer Vision, Bombay, India, 683-688, January 4-7, 1998
Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes
Jones, M. and T. Poggio
International Journal of Computer Vision, Vol. 29, No. 2, 107-131, 1998
Notes on PCA, Regularization, Sparsity and Support Vector Machines
Poggio, T. and F. Girosi
CBCL Paper #161/AI Memo #1632, Massachusetts Institute of Technology, Cambridge, MA, April 1998
Object Recognition with Support Vector Machines
Pontil, M. and A. Verri
In: IEEE Trans. Pattern Anal. Mach. Intell., Vol. 20, 637-646, 1998
On the Noise Model of Support Vector Machine Regression
Pontil, M., S. Mukherjee and F. Girosi
CBCL Paper #168, AI Memo #1651, Massachusetts Institute of Technology, Cambridge, MA, October 1998
Pre-attentive Segmentation in the Primary Visual Cortex
Li, Zhaoping
CBCL Paper #163/AI Memo #1640, Massachusetts Institute of Technology, Cambridge, MA, April 1998
RAPID: Randomized Pharmacophore Identification for Drug Design
Finn, P., Kavraki, L., Latombe, J.C., Motwani, R., Shelton, C., Venkatasubramanian, S. and Yao, A
Computational Geometry: Theory and Applications, Vol. 10, No. 4, 1998
Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision
Weiss, Yair and Edward H. Adelson
CBCL Paper #158/AI Memo 1624, Massachusetts Institute of Technology, Cambridge, MA, February 1998
Sparse Correlation Kernel Analysis and Reconstruction
Papageorgiou, C., F. Girosi and T. Poggio
CBCL Paper #162/AI Memo #1635, Massachusetts Institute of Technology, Cambridge, MA, May 1998
Statistical Models for Co-occurrence Data
Hofmann, Thomas and Jan Puzicha
CBCL Paper #161/AI Memo #1625, Massachusetts Institute of Technology, Cambridge, MA, February 1998
Three-Dimensional Correspondence
Shelton, C. (S.M. Thesis, EECS, MIT, May 1998):
Vetter, T. “Synthesis of Novel Views from a Single Face Image,”
International Journal of Computer Vision, Vol. 28, No. 2, 103-116, 1998
1997
A Bootstrapping Algorithm for Learning Linear Models of Object Classes
Vetter, T., M. Jones and T. Poggio
CBCL Paper #143/ AI Memo #1600, Massachusetts Institute of Technology, Cambridge, MA, February 1997
A Perturbation Scheme for Spherical Arrangements with Application to Molecular Modeling
Halperin, D. and Shelton, C.R
AI Laboratory Technical Report #1618, Massachusetts Institute of Technology, Cambridge, MA, 1997
A Trainable System for People Detection
Oren, M., C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio
In: Proceedings of the 1997 Image Understanding Workshop, New Orleans, LA, 207-214, May 1997
A View on Dyslexia
Geiger, G. and J.Y. Lettvin
CBCL Paper #148/AI Memo #1608, Massachusetts Institute of Technology, Cambridge, MA, June 1997
An Equivalence between Sparse Approximation and Support Vector Machines
Girosi, F
CBCL Paper #161/AI Memo #1606, Massachusetts Institute of Technology, Cambridge, MA, May 1997
Belief Propagation and Revision in Networks with Loops
Weiss, Yair
CBCL Paper #155/AI Memo #1616, Massachusetts Institute of Technology, Cambridge, MA, November 1997
Estimating Dependency Structure as a Hidden Variable
Meila, Marina, Michael I. Jordan, and Quaid Morris
CBCL Paper #151/AI Memo #1611, Massachusetts Institute of Technology, Cambridge, MA, June 1997
High Frequency Time Series Analysis and Prediction Using Markov Models
Papageorgiou, C
In: Proceedings of Computational Intelligence in Financial Engineering, New York, 182-188, March 1997
Image-Based View Synthesis
Avidan, S., T. Evgeniou, A. Shashua and T. Poggio
CBCL Paper #145/AI Memo #1603, Massachusetts Institute of Technology, Cambridge, MA, January 1997
Improved Training Algorithm for Support Vector Machines
Osuna, E., R. Freund, and F. Girosi
In: IEEE Neural Network for Signal Processing (NNSP’97), Amelia Island, FL, September 1997
Model-Based Matching by Linear Combinations of Prototype
Jones, M. and T. Poggio
In: Proceedings of the 1997 Image Understanding Workshop, New Orleans, LA, 1357-1365, 1997
Neural Models for Part-Whole Hierarchies
Riesenhuber, M. and P. Dayan
In: Advances in Neural Information Processing, MIT Press, 9, 17-23, 1997
Nonlinear Prediction of Chaotic Time Series using a Support Vector Machine
Mukherjee, S., E. Osuna, and F. Girosi
In: IEEE Neural Network for Signal Processing (NNSP’97), Amelia Island, FL, September 1997
On Degeneracy of linear Reconstruction from Three Views: Linear Line Complex and Applications
Stein, G.P. and A. Shashua
CBCL Paper #157/AI Memo #1620, Massachusetts Institute of Technology, Cambridge, MA, December 1997
On-center and Off-center Cell Competition Generates Oriented Receptive Fields from Non-oriented Stimuli in Kohonen’s Self-organizing Map
Riesenhuber, M., H.U. Bauer and T. Geisel
In: Computation in Neural Systems (CNS*96), Plenum, 471-476, 1997
Pedestrian Detection Using Wavelet Templates
Oren, M., C. Papageorgiou, P. Sinha, E. Osuna and T. Poggio
In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’97), Puerto Rico, 193-199, June 1997
Properties of Support Vector Machines
Pontil, P. and A. Verri
CBCL Paper #152/AI Memo #1612, Massachusetts Institute of Technology, Cambridge, MA, August 1997
RAPID: Randomized Pharmacophore Identification for Drug Design.
Finn, P., Kavraki, L., Latombe, J.C., Motwani, R., Shelton, C., Venkatasubramanian, S. and Yao, A
In: Proceedings of the 13th ACM Symposium on Computational Geometry, Nice, 324-333, 1997
Sparse Representations of Multiple Signals
Evgeniou, T. and T. Poggio
CBCL Paper #156/ AI Memo #1619, Massachusetts Institute of Technology, Cambridge, MA, September 1997
Support Vector Machines: Training and Applications
Osuna, E., R. Freund and F. Girosi
CBCL Paper #144/AI Memo #1602, Massachusetts Institute of Technology, Cambridge, MA, May 1997
Training Support Vector Machines: An Application to Face Detection
Osuna, E., R. Freund and F. Girosi
In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’97), Puerto Rico, 130-136, June 1997
Translation Invariance in Object Recognition, and its Relation to other Visual Transformations
Dill, M. and S. Edelman
CBCL Paper 150/ AI Memo 1610, Massachusetts Institute of Technology, Cambridge, MA, June 1997
Triangulation by Continuous Embedding
Meila, Marina and Michael I. Jordan
CBCL Paper #146/AI Memo #1605, Massachusetts Institute of Technology, Cambridge, MA, March 1997
Videorealistic Talking Faces: A Morphing Approach
Ezzat, T. and T. Poggio
In: Proceedings of the Audiovisual Speech Production Workshop, Rhodes, Greece, September 1997, in press
Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes
Edelman, S. and S. Duvdevani-Bar
CBCL Paper #154/ AI Memo #1615, Massachusetts Institute of Technology, Cambridge, MA, September 1997
Visual Segmentation without Classification in a Model of the Primary Visual Cortex
Li, Zhaoping
CBCL Paper #153/AI Memo 1613, Massachusetts Institute of Technology, Cambridge, MA, August 1997
1996
Analyzing Phase Transitions in High-Dimensional Self-Organizing Maps
Riesenhuber, M., H.U. Bauer and T. Geisel
Biological Cybernetics 75, 397-407, 1996
Analyzing the Formation of Structure in High-dimensional Self-organizing Maps Reveals Differences to Feature Map Models
Riesenhuber, M., H.U. Bauer and T. Geisel
In: Artificial Neural Networks – ICANN’96, Vol. 1112 of Lecture Notes in Computer Science, Springer, 409-411, 1996
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
Schoelkopf, B., K. Sung, C. Burges, F. Girosi, P. Niyogi, T. Poggio and V. Vapnik
CBCL Paper 142/AI Memo 1599, Massachusetts Institute of Technology, Cambridge, MA, December 1996
Computing Upper and Lower Bounds on Likelihoods in Intractable Networks
Jaakkola, Tommi S. and Michael I. Jordan
CBCL Paper #136/AI Memo #1571, Massachusetts Institute of Technology, Cambridge, MA, March 1996
Facial Analysis and Synthesis Using Image-based Models
Ezzat, T. and T. Poggio
In: Proceedings of the Workshop on the Algorithmic Foundations of Robotics, Toulouse, France, 449-467, August 1996
Facial Analysis and Synthesis Using Image-Based Models
Ezzat, T. and T. Poggio
In: Proceedings of the Second International Conference on Automatic Faces and Gesture Recognition, Killington, VT, 116-121, October 1996
Factorial Hidden Markov Models
Ghahramani, Zoubin and Michael I. Jordan
CBCL Paper #130/AI Memo #1561, Massachusetts Institute of Technology, Cambridge, MA, January 1996
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks
Jaakkola, Tommi S., Lawrence K. Saul, Michael I. Jordan
CBCL Paper #129/AI Memo #1560, Massachusetts Institute of Technology, Cambridge, MA, January 1996
Image Based Rendering Using Algebraic Techniques
Evgeniou, T
CBCL Paper 140/ AI Memo 1592, Massachusetts Institute of Technology, Cambridge, MA, November 1996
Image Representation for Visual Learning
Beymer, D. and T. Poggio
Science, 272, 1905-1909, 1996
Learning Linear, Sparse, Factorial Codes
Olshausen, Bruno A
CBCL Paper #138/AI Memo #1580, Massachusetts Institute of Technology, Cambridge, MA, July 1996
Mean Field Theory for Sigmoid Belief Networks
Saul, Lawrence K., Tommi Jaakkola and Michael I. Jordan
CBCL Paper #135/AI Memo #1570, Massachusetts Institute of Technology, Cambridge, MA, March 1996
Model-based Matching by Linear Combinations of Prototypes
Jones, M. and T. Poggio
CBCL Paper #139/AI Memo #1583, Massachusetts Institute of Technology, Cambridge, MA, September 1996
Networks that Learn for Image Understanding
Poggio, T. and K.K. Sung
In: Advances in Image Understanding, K. Bowyer and N. Ahuja (eds.), IEEE Computer Society Press, 226-240, 1996
Neural Networks
Jordan, Michael I. and Christopher M. Bishop
CBCL Paper #131/AI Memo #1562, Massachusetts Institute of Technology, Cambridge, MA, March 1996
On The Relationship between Generalization Error, Hypothesis Complexity and Sample Complexity for Radial Basis Functions
Niyogi, P. and F. Girosi
Neural Computation, 8, 819-842, 1996
Phase Diagrams of Self-Organizing Maps
Bauer, H.U., M. Riesenhuber and T. Geisel
Physical Review E 54, 2807-2810, 1996
Prior Information and Generalized Questions
Lemm, J.D
CBCL Paper #141/AI Memo #1598, Massachusetts Institute of Technology, Cambridge, MA, December 1996
Probabilistic Independence Networks for Hidden Markov Probability Models
Smyth, Padhraic, David Heckerman and Michael Jordan
CBCL Paper #132/AI Memo #1565, Massachusetts Institute of Technology, Cambridge, MA, February 1996
Reinforcement Learning by Probability Matching
Sabes, Philip N. and Michael I. Jordan
CBCL Paper #134/AI Memo #1568, Massachusetts Institute of Technology, Cambridge, MA, January 1996
1995
Approximation Error Bounds that Use VC-bounds
Girosi, F
In: Proceedings of the International Conference on Artificial Neural Networks, 295-302, Paris, October 9-13, 1995
Beyond Feature Map Models: Analyzing the Emergence of Ocular Dominance and Orientation Columns in High-dimensional Self-organizing Maps
Riesenhuber, M., H.U. Bauer and T. Geisel
In: Learning and Memory: Proceedings of the 23rd Göttingen Neurobiology Conference 1995, Georg Thieme Verlang, Stuttgart, Vol. 1, 1995
Face Recognition from One Example View
Beymer, D. and T. Poggio
CBCL Paper #121/AI Memo #1536, Massachusetts Institute of Technology, Cambridge, MA, September 1995
Finding Human Faces with a Gaussian Mixture Distribution-based Face Model
Sung, K.K. and T. Poggio
In: Proceedings of Second Asian Conference on Computer Vision, Singapore, December 1995
Minimizing Statistical Bias with Queries
Cohn, David A
CBCL Paper #124/AI Memo #1552, Massachusetts Institute of Technology, Cambridge, MA, September 1995
Model-based Matching of Line Drawings by Linear Combinations of Prototypes
Jones, M. and T. Poggio
CBCL Paper #128/AI Memo #1559, Massachusetts Institute of Technology, Cambridge, MA, December 1995
On the Physiology of Bistable Percepts
Logothetis, N. K. and D. A. Leopold
CBCL Paper #125/AI Memo #1553, Massachusetts Institute of Technology, Cambridge, MA, November 1995
Optical Flow from 1-D Correlation: Application to a Simple Time-to-Crash Detector
Ancona, N. and T. Poggio
International Journal of Computer Vision, Vol. 14, 131-146, 1995
Prior Knowledge and the Creation of Virtual Examples for RBF Networks
Girosi, F. and N. Chan
In: Neural Networks Signal Processing Proceedings of the 1995/IEEE-SP/Workshop, IEEE Signal Processing Society, Cambridge, MA, 201-210, September 1995
Regularization Theory and Neural Networks Architectures
Girosi, F., M. Jones, and T. Poggio
Neural Computation, Vol. 7, No. 2, 219-269, 1995
Template Matching: Matched Spatial Filters and Beyond
Brunelli, R. and T. Poggio
CBCL Paper 123/AI Memo 1549, Massachusetts Institute of Technology, Cambridge, MA, October 1995
The Role of Attention in Binocular Rivalry as Revealed through Optokinetic Nystagmus
Leopold, D. A., J. C. Fitzgibbons, and N. K. Logothetis
CBCL Paper #126/AI Memo #1554, Massachusetts Institute of Technology, Cambridge, MA, November 1995
Vector-Space Integration of Local and Long-Range Information in Visual Cortex
Somers, David C., Emanuel V. Todorov, Athanassios G. Siapas and Mgriganka Sur
CBCL Paper #127/AI Memo #1556, Massachusetts Institute of Technology, Cambridge, MA, November 1995
Vectorizing Face Images by Interleaving Shape and Texture Computations
Beymer, D
CBCL Paper #122/AI Memo #1537, Massachusetts Institute of Technology, Cambridge, MA, September 1995
1994
A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks
Hutchinson, J.M., A. Lo and T. Poggio
Journal of Finance, Vol. XLIX, No. 3, 851-889, 1994
L\92Intelligenza e Saper Imparare
Poggio, T.\A0
Sistemi & Impresa, No. 4, 19-22, 1994
Symmetric 3D Objects are an Easy Case for 2D Object Recognition
Vetter, T. and T. Poggio.\A0
Spatial Vision, 8, No. 4, 443-453, 1994
The Importance of Symmetry and Virtual Views in Three-dimensional Object Recognition
Vetter, T., T. Poggio and H. B\FClthoff
Current Biology, 4, No. 1, 18-23, 1994
View-dependent Object Recognition by Monkeys
Logothetis, N.K., J. Pauls, H. B\FClthoff and T. Poggio
Current Biology, 4, No. 5, 401-414, 1994
1993
Caricatural Effects in Automated Face Perception
Brunelli, R. and T. Poggio.\A0
Biological Cybernetics, 69, 235-241, 1993
Face Recognition: Features Versus Templates
Brunelli, R. and T. Poggio
IEEE PAMI, 15, 1042-1052, 1993
In Memorium: Werner Reichardt, 1924-1992
Poggio, T.\A0
Biological Cybernetics, 69, 1, 1-3, 1993
1992
A Project for an Intelligent System: Vision and Learning
Poggio, T. and L. Stringa.\A0
International Journal of Quantum Chemistry, 42, 727-739, 1992
Analog VLSI Systems for Image Acquisition and Fast Early Vision Processing
Wyatt, J.L., C. Keast, M. Seidel, D. Standley, B. Horn, T. Knight, C. Sodini, H.-S. Lee and T. Poggio).\A0
International Journal of Computer Vision, 8, No. 3, 217-230, 1992
Bringing the Grandmother Back into the Picture: A Memory-based View of Object Recognition
Edelman, S. and T. Poggio.\A0
International Journal of Pattern Recognition of Artificial Intelligence, 6, 37-61, April, 1992
Fast Perceptual Learning in Visual Hyperacuity
Poggio, T., M. Fahle and S. Edelman
Science, 256, 1018-1021, May 1992
Learning of Visual Modules from Examples: A Framework for Understanding Adaptive Visual Performance
Poggio, T., S. Edelman and M. Fahle
Computer Vision, Graphics and Image Processing B: Image Understanding, 56, No. 1, 22-30,1992
1991
Models of Object Recognition
Edelman, S. and T. Poggio.\A0
Current Opinion in Neurobiology, 1, 270-273, 1991
Parallel Processing in the DARPA Strategic Computing Vision Program
Weems, C., C. Brown, J. Webb, and J. Kender
IEEE Expert, 6, 23-38, October 1991
1990
A Network that Learns to Recognize 3D Objects
Edelman, S. and T. Poggio.\A0
Nature, 343, 263-266, 1990
Networks and the Best Approximation Property
Girosi, F. and T. Poggio.\A0
Biological Cybernetics, 63, 3, 169-176, 1990
Networks for Approximation and Learning
Poggio, T. and F. Girosi.\A0
Proceedings of theIEEE (special issue: Neural Networks I: Theory and Modeling), Vol. 78, No. 9, 1481-1497, September 1990
Regularization Algorithms for Learning that are Equivalent to Multilayer Networks
Poggio, T. and F. Girosi.\A0
Science, 247, 978-982, 1990
Vision
Kanade, K., T. Binford, T. Poggio and A. Rosenfeld
Annual Review of Computer Science 4, 517-529, 1990
1989
A Parallel Algorithm for Real Time Computation of Optical Flow
B\FClthoff, H.H., J. Little and T. Poggio
Nature, 337, 549-553, 1989
Integrating Visual Cues for Object Segmentation and Recognition
Edelman, S. and T. Poggio.\A0
Optics News, 15, 8-16, 1989
Integration of Vision Modules and Labelling of Surface Discontinuities
Gamble, E., D. Geiger, T. Poggio and D. Weinshall.\A0
IEEE Trans. Systems, Man & Cybernetics, 19, 6, 1576-1581, 1989
Motion Field and Optical Flow: Qualitative Properties
Verri, A. and T. Poggio.\A0
IEEE Trans. PAMI, 11, 490-498, 1989
Oltre l\92Immagine\94 or \93Beyond the Image
Poggio, T.\A0
Ulisse 2000, 63, 76-86, June 1989
Rendere le Macchine (e l\92Intelligenza Artificiale) in Grado di Vedere
Hurlbert, A. and T. Poggio.\A0
Sistemi Intelligenti, 1, 75-103, 1989
Representation Properties of Networks: Kolmogorov\92s Theorem is Irrelevant
Girosi, F. and T. Poggio
Neural Computation, 1, 465-469, 1989
Visione Biologica and Visione Artifiale
Poggio, T.\A0
SFERA, 6, 164-5, 1989
1988
A Regularized Solution to Edge Detection
Poggio, T., H. Voorhees and A. Yuille
Journal of Complexity, 4, 106-123, 1988
Computing Texture Boundaries from Images
Voorhees, H. and T. Poggio.\A0
Nature, 333, 364-367, 1988
Ill-posed Problems in Early Vision
Bertero, M., T. Poggio and V. Torre
Proceedings of the IEEE, 76, 869-889, 1988
Parallel Integration of Vision Modules
Poggio, T., E. Gamble and J. Little
Science, 242, 436-440, 1988
Seeing in Parallel: The Vision Machine
Little, J., T. Poggio, and E.B. Gamble
International Journal of Supercomputer Applications, 2, 4, 13-28, 1988
Synthesizing a Color Algorithm from Examples
Hurlbert, A. and T. Poggio.\A0
Science, 239, 482-485, 1988
1987
Probabilistic Solution of Ill-posed Problems in Computational Vision
Marroquin, J., S. Mitter and T. Poggio
Journal of American Statistical Association, 82, 76-89, 1987
Synapses that Compute Motion
Poggio, T. and C. Koch.\A0
Scientific American, 256, 46-52, 1987
1986
Computations in the Vertebrate Retina: Gain Enhancement, Differentiation and Motion Discrimination
Koch, C., V. Torre and T. Poggio
Trends in Neurosciences, 9, 204-211, 1986
Do Computers Need Attention?
Hurlbert, A. and T. Poggio.\A0
Nature, 321, 651-652, 1986
On Edge Detection
Torre, V. and T. Poggio.\A0
IEEE Trans. PAMI, 8, 147-163, 1986
Scaling Theorems for Zero Crossings
Yuille, A.L. and T. Poggio.\A0
IEEE Trans. PAMI, 8, 15-25, 1986
1985
A Simple Algorithm for Solving the Cable Equation in Dendritic Trees of Arbitrary Geometry
Koch, C. and T. Poggio
J. Neuroscience Methods, 12, 303-315, 1985
Computational Vision and Regularization Theory
Poggio, T., V. Torre and C. Koch
Nature, 317, 314-319, 1985
Early Vision: From Computational Structure to Algorithms and Parallel Hardware
Poggio, T.\A0
Computer Vision, Graphics, and Image Processing, 31, 139-155, 1985
Fingerprints Theorems for Zero Crossing
Yuille, A. and T. Poggio.\A0
J. Optical Society America A, 2, 683-692, 1985
Ill-posed Problems in Early Vision: From Computational Theory to Analog Networks
Poggio, T. and C. Koch.\A0
Proceedings of the Royal Society London B, 226, 303-323, 1985
Spotlight on Attention
Hurlbert, A. and T. Poggio.\A0
Trends in Neurosciences, 8, 309-311, 1985
The Biophysical Properties of Spines as a Basis for their Electrical Function: A Comment on Kawato and Tsukahara, 1983
Koch, C. and T. Poggio.\A0
Journal of Theoretical Biology, 113, 225-229, 1985
1984
The Analysis of Stereopsis
Poggio, G. and T. Poggio.\A0
Annual Review of Neuroscience, 7, 379-412, 1984
Vertical Image Registration in Stereopsis
Nielsen, K.R.K. and T. Poggio
Vision Research, 24, 1133-1140, 1984
Vision by Man and Machine
Poggio, T.\A0
Scientific American, 250, 106-116, 1984
1983
A Theoretical Analysis of Electrical Properties of Spines
Koch, C. and T. Poggio.\A0
Proceedings of the Royal Society London B, 218, 455-477, 1983
Electrical Properties of Dendritic Spines
Koch, C. and T. Poggio.\A0
Trends in Neurosciences, 6, 80-83, 1983
Figure-ground Discrimination by Relative Movement in the Visual System of the Fly – II: Towards the Neural Circuitry
Reichardt, W., T. Poggio and K. Hausen
Biological Cybernetics, 46, 1-30, 1983
Nonlinear Interactions in a Dendritic Tree: Localization, Timing and Role in Information Processing
Koch, C., T. Poggio and V. Torre
PNAS, 80, 2799-2802, 1983
1982
B\FClthoff, H.
T. Poggio, and C. Wehrhahn. “3D Analysis of the Flight Trajectories of Flies (Drosophilia Melanogaster) .” Z. Naturforsch 35c (1980). Google Scholar
Crick, F. H. C.
D. C. Marr, and T. Poggio. “An Information Processing Approach to Understanding the Visual Cortex.” A.I. Memo No. 557 (1980). Google Scholar
Hidden Cues in Random-line Stereograms
Nishihara, H.K. and T. Poggio
Nature, 300, 347-349, 1982
Retinal Ganglion Cells: A Functional Interpretation of Dendritic Morphology
Koch, C., T. Poggio and V. Torre
Proceedings of the Royal Society London, 298, 227-264, 1982
Tracking and Chasing in Houseflies (Musca): An Analysis of 3D Flight Trajectories
Wehrhahn, C., T. Poggio and T. B\FClthoff
Biological Cybernetics, 45, 123-130, 1982
1981
A Neuronal Circuitry for Relative Movement Discrimination by the Visual System of the Fl
Poggio, T., W. Reichardt and W. Hausen
y, Naturwissenschaften, 68, 9, 443-466, 1981
Asymptotic Oscillations in the Tracking Behavior of the Fly Musca Domestic
Geiger, G. and T. Poggio.\A0
a, Biological Cybernetics, 41, 197-201, 1981
Marr\92s Computational Approach to Vision
Poggio, T.\A0
Trends in Neurosciences, 10, 258-262, 1981
Visual Fixation and Tracking by Flies: Mathematical Properties of Simple Control Systems
Poggio, T. and W. Reichardt.\A0
Biological Cybernetics, 40, 101-112, 1981
Visual Hyperacuity: Spatiotemporal Interpolation in Human Vision
Fahle, M. and T. Poggio.\A0
Proceedings of the Royal Society London B, 213, 451-477, 1981
1979
Reichrdt, W.
and T. Poggio. “Figure-Ground Discrimination by Relative Movement in the Visual System of the Fly.” Biological Cybernetics 35 (1979). Google Scholar
1978
Marr, D.
G. Palm, and T. Poggio. “Analysis of a Cooperative Stereo Algorithm.” Biological Cybernetics 28, no. 4 (1978). Google Scholar
1977
1975
Geiger, G.
and T. Poggio. “The Orientation of Flies Towards Visual Patterns: On the Search for the Underlying Functional Interactions.” Biological Cybernetics 19 (1975). Google Scholar
Reichardt, W.
and T. Poggio. “A Theory of the Pattern Induced Flight Orientation of the Fly Musca Domestica II.” 94 Biological Cybernetics 18 (1975). Google Scholar
1973
Borsellino, A.
and T. Poggio. “Convolution and Correlation Algebras.” Kybernetik 13, no. 2 (1973).Google Scholar
Considerations on Models of Movement Detection
“
” Kybernetik 13, no. 4 (1973). Google Scholar
Poggio, T.
“On Holographic Models of Memory.” Kybernetik 12, no. 4 (1973). Google Scholar
1972
A Theory of the Pattern Induced Flight Orientation of the Fly Musca Domestica
Poggio, T., and W Reichardt. “
” Kybernetik 10, no. 1 (1972). Google Scholar