Fingerprints Theorems. - Alan L. Yuille; Tomaso A. Poggio (AAAI, 1984)

Early vision: From computational structure to algorithms and parallel hardware. - Tomaso A. Poggio (Comput. Vis. Graph. Image Process., 1985)

On parallel stereo. - Michael Drumheller; Tomaso A. Poggio (ICRA, 1986)

On Edge Detection. - Vincent Torre; Tomaso A. Poggio (IEEE Trans. Pattern Anal. Mach. Intell., 1986)

Scaling Theorems for Zero Crossings. - Alan L. Yuille; Tomaso A. Poggio (IEEE Trans. Pattern Anal. Mach. Intell., 1986)

An Optimal Scale for Edge Detection. - Davi Geiger; Tomaso A. Poggio (IJCAI, 1987)

Learning a Color Algorithm from Examples. - Tomaso A. Poggio; Anya C. Hurlbert (NIPS, 1987)

Parallel Optical Flow Using Local Voting. - James J. Little; Heinrich H. Bülthoff; Tomaso A. Poggio (ICCV, 1988)

A Network for Image Segmentation Using Color. - Anya C. Hurlbert; Tomaso A. Poggio (NIPS, 1988)

A regularized solution to edge detection. - Tomaso A. Poggio; Harry Voorhees; Alan L. Yuille (J. Complex., 1988)

Representation properties of multilayer feedforward networks. - B. Moore; Tomaso A. Poggio (Neural Networks, 1988)

Learning, regularization and splines. - Tomaso A. Poggio (Neural Networks, 1988)

Ill-posed problems in early vision. - Mario Bertero; Tomaso A. Poggio; Vincent Torre (Proc. IEEE, 1988)

Representation Properties of Networks: Kolmogorov's Theorem Is Irrelevant. - Federico Girosi; Tomaso A. Poggio (Neural Comput., 1989)

Motion Field and Optical Flow: Qualitative Properties. - Alessandro Verri; Tomaso A. Poggio (IEEE Trans. Pattern Anal. Mach. Intell., 1989)

Integration of vision modules and labeling of surface discontinuities. - Edward B. Gamble; Davi Geiger; Tomaso A. Poggio; Daphna Weinshall (IEEE Trans. Syst. Man Cybern., 1989)

Extensions of a Theory of Networks for Approximation and Learning. - Federico Girosi; Tomaso A. Poggio; Bruno Caprile (NIPS, 1990)

Networks for approximation and learning. - Tomaso A. Poggio; Federico Girosi (Proc. IEEE, 1990)

HyperBF Networks for Real Object Recognition. - Roberto Brunelli; Tomaso A. Poggio (IJCAI, 1991)

Parallel Processing in the DARPA Strategic Computing Vision Program. - Charles C. Weems; Christopher M. Brown; Jon A. Webb; Tomaso A. Poggio; John R. Kender (IEEE Expert, 1991)

Face Recognition through Geometrical Features. - Roberto Brunelli; Tomaso A. Poggio (ECCV, 1992)

Learning of visual modules from examples: A framework for understanding adaptive visual performance. - Tomaso A. Poggio; Shimon Edelman; Manfred Fahle (CVGIP Image Underst., 1992)

Analog VLSI systems for image acquisition and fast early vision processing. - John L. Wyatt Jr.; Craig L. Keast; Mark Seidel; David L. Standley; Berthold K. P. Horn; Tom Knight; Charles G. Sodini; Hae-Seung Lee; Tomaso A. Poggio (Int. J. Comput. Vis., 1992)

Bringing the Grandmother back into the Picture: A Memory-Based View of Object Recognition. - Shimon Edelman; Tomaso A. Poggio (Int. J. Pattern Recognit. Artif. Intell., 1992)

Optical flow from 1D correlation: Application to a simple time-to-crash detector. - Nicola Ancona; Tomaso A. Poggio (ICCV, 1993)

Caricatural effects in automated face perception. - Roberto Brunelli; Tomaso A. Poggio (Biol. Cybern., 1993)

Werner Reichardt 1924-1992. - Tomaso A. Poggio (Biol. Cybern., 1993)

Face Recognition: Features Versus Templates. - Roberto Brunelli; Tomaso A. Poggio (IEEE Trans. Pattern Anal. Mach. Intell., 1993)

Report on Workshop on High Performance Computing and Communications for Grand Challenge Applications: Computer Vision, Speech and Natural Language Processing, and Artificial Intelligence. - Benjamin W. Wah; Thomas S. Huang; Aravind K. Joshi; Dan I. Moldovan; Yiannis Aloimonos; Ruzena Bajcsy; Dana H. Ballard; Doug DeGroot; Kenneth A. De Jong; Charles R. Dyer; Scott E. Fahlman; Ralph Grishman; et al. (IEEE Trans. Knowl. Data Eng., 1993)

Introduction to the Special Section on Learning in Computer Vision. - Bir Bhanu; Tomaso A. Poggio (IEEE Trans. Pattern Anal. Mach. Intell., 1994)

Finding Human Faces with a Gaussian Mixture Distribution-Based Face Model. - Tomaso A. Poggio; Kah Kay Sung (ACCV, 1995)

Learning Human Face Detection in Cluttered Scenes. - Kah Kay Sung; Tomaso A. Poggio (CAIP, 1995)

Face Recognition from One Example View. - David Beymer; Tomaso A. Poggio (ICCV, 1995)

Model-Based Matching of Line Drawings by Linear Combinations of Prototypes. - Michael J. Jones 001; Tomaso A. Poggio (ICCV, 1995)

Optical flow from 1-D correlation: Application to a simple time-to-crash detector. - Nicola Ancona; Tomaso A. Poggio (Int. J. Comput. Vis., 1995)

Automatic person recognition by acoustic and geometric features. - Roberto Brunelli; Daniele Falavigna; Tomaso A. Poggio; Luigi Stringa (Mach. Vis. Appl., 1995)

Regularization Theory and Neural Networks Architectures. - Federico Girosi; Michael J. Jones 001; Tomaso A. Poggio (Neural Comput., 1995)

Image Synthesis from a Single Example Image. - Thomas Vetter; Tomaso A. Poggio (ECCV (1), 1996)

Facial Analysis and Synthesis Using Image-Based Models. - Tony Ezzat; Tomaso A. Poggio (FG, 1996)

3D Object Recognition: A Model of View-Tuned Neurons. - Emanuela Bricolo; Tomaso A. Poggio; Nikos K. Logothetis (NIPS, 1996)

Image Representations for Visual Learning (Invited Paper). - Tomaso A. Poggio (AVBPA, 1997)

Videorealistic talking faces: a morphing approach. - Tony Ezzat; Tomaso A. Poggio (AVSP, 1997)

Pedestrian Detection Using Wavelet Templates. - Michael Oren; Constantine Papageorgiou; Pawan Sinha; Edgar Osuna; Tomaso A. Poggio (CVPR, 1997)

A bootstrapping algorithm for learning linear models of object classes. - Thomas Vetter; Michael J. Jones 001; Tomaso A. Poggio (CVPR, 1997)

Just One View: Invariances in Inferotemporal Cell Tuning. - Maximilian Riesenhuber; Tomaso A. Poggio (NIPS, 1997)

Image-based view synthesis by combining trilinear tensors and learning techniques. - Shai Avidan; Theodoros Evgeniou; Amnon Shashua; Tomaso A. Poggio (VRST, 1997)

Linear Object Classes and Image Synthesis From a Single Example Image. - Thomas Vetter; Tomaso A. Poggio (IEEE Trans. Pattern Anal. Mach. Intell., 1997)

Template matching: matched spatial filters and beyond. - Roberto Brunelli; Tomaso A. Poggio (Pattern Recognit., 1997)

Guest Editors' Introduction: Neural Networks For Signal Processing. - Anthony G. Constantinides; Simon Haykin 001; Yu Hen Hu; Jenq-Neng Hwang; Shigeru Katagiri; Sun-Yuan Kung; Tomaso A. Poggio (IEEE Trans. Signal Process., 1997)

Comparing support vector machines with Gaussian kernels to radial basis function classifiers. - Bernhard Schölkopf; Kah Kay Sung; Christopher J. C. Burges; Federico Girosi; Partha Niyogi; Tomaso A. Poggio; Vladimir Vapnik (IEEE Trans. Signal Process., 1997)

MikeTalk: A Talking Facial Display Based on Morphing Visemes. - Tony Ezzat; Tomaso A. Poggio (CA, 1998)

Hierarchical Morphable Models. - Michael J. Jones 001; Tomaso A. Poggio (CVPR, 1998)

Multidimensional Morphable Models. - Michael J. Jones 001; Tomaso A. Poggio (ICCV, 1998)

A General Framework for Object Detection. - Constantine Papageorgiou; Michael Oren; Tomaso A. Poggio (ICCV, 1998)

Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes. - Michael J. Jones 001; Tomaso A. Poggio (Int. J. Comput. Vis., 1998)

A Sparse Representation For Function Approximation. - Tomaso A. Poggio; Federico Girosi (Neural Comput., 1998)

Example-Based Learning for View-Based Human Face Detection. - Kah Kay Sung; Tomaso A. Poggio (IEEE Trans. Pattern Anal. Mach. Intell., 1998)

Incorporating prior information in machine learning by creating virtual examples. - Partha Niyogi; Federico Girosi; Tomaso A. Poggio (Proc. IEEE, 1998)

Guest Editorial Applications Of Artificial Neural Networks To Image Processing. - Rama Chellappa; Kunihiko Fukushima; Aggelos K. Katsaggelos; Sun-Yuan Kung; Yann LeCun; Nasser M. Nasrabadi; Tomaso A. Poggio (IEEE Trans. Image Process., 1998)

Sparse correlation kernel reconstruction. - Constantine Papageorgiou; Federico Girosi; Tomaso A. Poggio (ICASSP, 1999)

A Pattern Classification Approach to Dynamical Object Detection. - Constantine Papageorgiou; Tomaso A. Poggio (ICCV, 1999)

Trainable Pedestrian Detection. - Constantine Papageorgiou; Tomaso A. Poggio (ICIP (4), 1999)

Machine Learning, Machine Vision, and the Brain. - Tomaso A. Poggio; Christian R. Shelton (AI Mag., 1999)

GEM: A Global Electronic Market System. - Benny Rachlevsky-Reich; Israel Ben-Shaul; Nicholas Tung Chan; Andrew W. Lo; Tomaso A. Poggio (Inf. Syst., 1999)

CBF: A New Framework for Object Categorization in Cortex. - Maximilian Riesenhuber; Tomaso A. Poggio (Biologically Motivated Computer Vision, 2000)

Learning-Based Approach to Real Time Tracking and Analysis of Faces. - Vinay P. Kumar; Tomaso A. Poggio (FG, 2000)

Bounds on the Generalization Performance of Kernel Machine Ensembles. - Theodoros Evgeniou; Luis Pérez-Breva; Massimiliano Pontil; Tomaso A. Poggio (ICML, 2000)

Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation. - Chikahito Nakajima; Norihiko Itoh; Massimiliano Pontil; Tomaso A. Poggio (ICPR, 2000)

People Recognition and Pose Estimation in Image Sequences. - Chikahito Nakajima; Massimiliano Pontil; Tomaso A. Poggio (IJCNN (4), 2000)

Incremental and Decremental Support Vector Machine Learning. - Gert Cauwenberghs; Tomaso A. Poggio (NIPS, 2000)

Feature Selection for SVMs. - Jason Weston; Sayan Mukherjee 001; Olivier Chapelle; Massimiliano Pontil; Tomaso A. Poggio; Vladimir Vapnik (NIPS, 2000)

Regularization Networks and Support Vector Machines. - Theodoros Evgeniou; Massimiliano Pontil; Tomaso A. Poggio (Adv. Comput. Math., 2000)

Statistical Learning Theory: A Primer. - Theodoros Evgeniou; Massimiliano Pontil; Tomaso A. Poggio (Int. J. Comput. Vis., 2000)

Visual Speech Synthesis by Morphing Visemes. - Tony Ezzat; Tomaso A. Poggio (Int. J. Comput. Vis., 2000)

Morphable Models for the Analysis and Synthesis of Complex Motion Patterns. - Martin A. Giese; Tomaso A. Poggio (Int. J. Comput. Vis., 2000)

A Trainable System for Object Detection. - Constantine Papageorgiou; Tomaso A. Poggio (Int. J. Comput. Vis., 2000)

Introduction: Learning and Vision at CBCL. - Tomaso A. Poggio; Alessandro Verri (Int. J. Comput. Vis., 2000)

Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images. - Bernd Heisele; Thomas Serre; Sayan Mukherjee 001; Tomaso A. Poggio (CVPR (2), 2001)

Component-based Face Detection. - Bernd Heisele; Thomas Serre; Massimiliano Pontil; Tomaso A. Poggio (CVPR (1), 2001)

Face Recognition with Support Vector Machines: Global versus Component-based Approach. - Bernd Heisele; Purdy Ho; Tomaso A. Poggio (ICCV, 2001)

Categorization by Learning and Combining Object Parts. - Bernd Heisele; Thomas Serre; Massimiliano Pontil; Thomas Vetter; Tomaso A. Poggio (NIPS, 2001)

Example-Based Object Detection in Images by Components. - Anuj Mohan; Constantine Papageorgiou; Tomaso A. Poggio (IEEE Trans. Pattern Anal. Mach. Intell., 2001)

Visual Categorization: How the Monkey Brain Does It. - Ulf Knoblich; Maximilian Riesenhuber; David J. Freedman; Earl K. Miller; Tomaso A. Poggio (Biologically Motivated Computer Vision, 2002)

Recognizing Expressions by Direct Estimation of the Parameters of a Pixel Morphable Model. - Vinay P. Kumar; Tomaso A. Poggio (Biologically Motivated Computer Vision, 2002)

On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision. - Thomas Serre; Maximilian Riesenhuber; Jennifer Louie; Tomaso A. Poggio (Biologically Motivated Computer Vision, 2002)

Attentional Selection for Object Recognition - A Gentle Way. - Dirk B. Walther; Laurent Itti; Maximilian Riesenhuber; Tomaso A. Poggio; Christof Koch (Biologically Motivated Computer Vision, 2002)

Biophysiologically Plausible Implementations of the Maximum Operation. - Angela J. Yu; Martin A. Giese; Tomaso A. Poggio (Neural Comput., 2002)

Learning and vision machines. - Bernd Heisele; Alessandro Verri; Tomaso A. Poggio (Proc. IEEE, 2002)

Trainable videorealistic speech animation. - Tony Ezzat; Gadi Geiger; Tomaso A. Poggio (ACM Trans. Graph., 2002)

Learning and Perceptual Interfaces. - Tomaso A. Poggio (CVPR Workshops, 2003)

Reanimating Faces in Images and Video. - Volker Blanz; Curzio Basso; Tomaso A. Poggio; Thomas Vetter (Comput. Graph. Forum, 2003)

Face recognition: component-based versus global approaches. - Bernd Heisele; Purdy Ho; Jane Wu; Tomaso A. Poggio (Comput. Vis. Image Underst., 2003)

Hierarchical classification and feature reduction for fast face detection with support vector machines. - Bernd Heisele; Thomas Serre; Sam Prentice; Tomaso A. Poggio (Pattern Recognit., 2003)

Full-body person recognition system. - Chikahito Nakajima; Massimiliano Pontil; Bernd Heisele; Tomaso A. Poggio (Pattern Recognit., 2003)

An Analytical Method for Multiclass Molecular Cancer Classification. - Ryan M. Rifkin; Sayan Mukherjee 001; Pablo Tamayo; Sridhar Ramaswamy; Chen-Hsiang Yeang; Michael Angelo; Michael Reich; Tomaso A. Poggio; Eric S. Lander; Todd R. Golub; Jill P. Mesirov (SIAM Rev., 2003)

Image Representations and Feature Selection for Multimedia Database Search. - Theodoros Evgeniou; Massimiliano Pontil; Constantine Papageorgiou; Tomaso A. Poggio (IEEE Trans. Knowl. Data Eng., 2003)

Oriented Filters for Object Recognition: an Empirical Study. - Jerry Jun Yokono; Tomaso A. Poggio (FGR, 2004)

Object Recognition with Features Inspired by Visual Cortex. - Thomas Serre; Lior Wolf; Tomaso A. Poggio (CVPR (2), 2005)

Learning Features of Intermediate Complexity for the Recognition of Biological Motion. - Rodrigo Sigala; Thomas Serre; Tomaso A. Poggio; Martin A. Giese (ICANN (1), 2005)

Morphing spectral envelopes using audio flow. - Tony Ezzat; Ethan Meyers; James R. Glass; Tomaso A. Poggio (INTERSPEECH, 2005)

Foreword. - Lenore Blum; Felipe Cucker; Tomaso A. Poggio; James Renegar; Michael Shub (Found. Comput. Math., 2005)

A Multiview Face Identification Model With No Geometric Constraints. - Jerry Jun Yokono; Tomaso A. Poggio (FGR, 2006)

Max-Gabor analysis and synthesis of spectrograms. - Tony Ezzat; Jake V. Bouvrie; Tomaso A. Poggio (INTERSPEECH, 2006)

Biophysical Models of Neural Computation: Max and Tuning Circuits. - Ulf Knoblich; Jake V. Bouvrie; Tomaso A. Poggio (WImBI, 2006)

Neuroscience: New Insights for AI? - Tomaso A. Poggio (WImBI, 2006)

Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization. - Sayan Mukherjee 001; Partha Niyogi; Tomaso A. Poggio; Ryan M. Rifkin (Adv. Comput. Math., 2006)

AM-FM Demodulation of Spectrograms using Localized 2D Max-Gabor Analysis. - Tony Ezzat; Jake V. Bouvrie; Tomaso A. Poggio (ICASSP (4), 2007)

A Biologically Inspired System for Action Recognition. - Hueihan Jhuang; Thomas Serre; Lior Wolf; Tomaso A. Poggio (ICCV, 2007)

Spectro-temporal analysis of speech using 2-d Gabor filters. - Tony Ezzat; Jake V. Bouvrie; Tomaso A. Poggio (INTERSPEECH, 2007)

A Component-based Framework for Face Detection and Identification. - Bernd Heisele; Thomas Serre; Tomaso A. Poggio (Int. J. Comput. Vis., 2007)

Robust Object Recognition with Cortex-Like Mechanisms. - Thomas Serre; Lior Wolf; Stanley M. Bileschi; Maximilian Riesenhuber; Tomaso A. Poggio (IEEE Trans. Pattern Anal. Mach. Intell., 2007)

Localized spectro-temporal cepstral analysis of speech. - Jake V. Bouvrie; Tony Ezzat; Tomaso A. Poggio (ICASSP, 2008)

Discriminative word-spotting using ordered spectro-temporal patch features. - Tony Ezzat; Tomaso A. Poggio (SAPA@INTERSPEECH, 2008)

A Canonical Neural Circuit for Cortical Nonlinear Operations. - Minjoon Kouh; Tomaso A. Poggio (Neural Comput., 2008)

On Invariance in Hierarchical Models. - Jake V. Bouvrie; Lorenzo Rosasco; Tomaso A. Poggio (NIPS, 2009)

Trainable, vision-based automated home cage behavioral phenotyping. - Hueihan Jhuang; Estíbaliz Garrote; Nicholas Edelman; Tomaso A. Poggio; Andrew Steele; Thomas Serre (MB, 2010)

Hierarchical Learning Machines and Neuroscience of Visual Cortex. - Tomaso A. Poggio (ECML/PKDD (1), 2010)

A neuromorphic approach to computer vision. - Thomas Serre; Tomaso A. Poggio (Commun. ACM, 2010)

Mathematics of the Neural Response. - Steve Smale; Lorenzo Rosasco; Jake V. Bouvrie; Andrea Caponnetto; Tomaso A. Poggio (Found. Comput. Math., 2010)

Attention as a Bayesian inference process. - Sharat Chikkerur; Thomas Serre; Cheston Tan; Tomaso A. Poggio (Human Vision and Electronic Imaging, 2011)

HMDB: A large video database for human motion recognition. - Hildegard Kuehne; Hueihan Jhuang; Estíbaliz Garrote; Tomaso A. Poggio; Thomas Serre (ICCV, 2011)

Why The Brain Separates Face Recognition From Object Recognition. - Joel Z. Leibo; Jim Mutch; Tomaso A. Poggio (NIPS, 2011)

Online Learning, Stability, and Stochastic Gradient Descent - Tomaso A. Poggio; Stephen Voinea; Lorenzo Rosasco (CoRR, 2011)

Learning Manifolds with K-Means and K-Flats. - Guillermo D. Cañas; Tomaso A. Poggio; Lorenzo Rosasco (NIPS, 2012)

Multiclass Learning with Simplex Coding. - Youssef Mroueh; Tomaso A. Poggio; Lorenzo Rosasco; Jean-Jacques E. Slotine (NIPS, 2012)

Learning and disrupting invariance in visual recognition with a temporal association rule. - Leyla Isik; Joel Z. Leibo; Tomaso A. Poggio (Frontiers Comput. Neurosci., 2012)

On Learnability, Complexity and Stability. - Silvia Villa; Lorenzo Rosasco; Tomaso A. Poggio (Empirical Inference, 2013)

The Computational Magic of Pattern Recognition in Cortex: A Theory of Selectivity and Invariance. - Tomaso A. Poggio (ICPRAM, 2013)

Learning invariant representations and applications to face verification. - Qianli Liao; Joel Z. Leibo; Tomaso A. Poggio (NIPS, 2013)

Neural representation of action sequences: how far can a simple snippet-matching model take us? - Cheston Tan; Jedediah M. Singer; Thomas Serre; David L. Sheinberg; Tomaso A. Poggio (NIPS, 2013)

Unsupervised Learning of Invariant Representations in Hierarchical Architectures. - Fabio Anselmi; Joel Z. Leibo; Lorenzo Rosasco; Jim Mutch; Andrea Tacchetti; Tomaso A. Poggio (CoRR, 2013)

Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? - Qianli Liao; Joel Z. Leibo; Youssef Mroueh; Tomaso A. Poggio (CoRR, 2013)

Models of visual cortex. - Tomaso A. Poggio; Thomas Serre (Scholarpedia, 2013)

Throwing Down the Visual Intelligence Gauntlet. - Cheston Tan; Joel Z. Leibo; Tomaso A. Poggio (Machine Learning for Computer Vision, 2013)

A deep representation for invariance and music classification. - Chiyuan Zhang; Georgios Evangelopoulos; Stephen Voinea; Lorenzo Rosasco; Tomaso A. Poggio (ICASSP, 2014)

Word-level invariant representations from acoustic waveforms. - Stephen Voinea; Chiyuan Zhang; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (INTERSPEECH, 2014)

Phone classification by a hierarchy of invariant representation layers. - Chiyuan Zhang; Stephen Voinea; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (INTERSPEECH, 2014)

Subtasks of Unconstrained Face Recognition. - Joel Z. Leibo; Qianli Liao; Tomaso A. Poggio (VISAPP (2), 2014)

Learning An Invariant Speech Representation. - Georgios Evangelopoulos; Stephen Voinea; Chiyuan Zhang; Lorenzo Rosasco; Tomaso A. Poggio (CoRR, 2014)

Unsupervised learning of clutter-resistant visual representations from natural videos. - Qianli Liao; Joel Z. Leibo; Tomaso A. Poggio (CoRR, 2014)

Computational role of eccentricity dependent cortical magnification. - Tomaso A. Poggio; Jim Mutch; Leyla Isik (CoRR, 2014)

Neural tuning size is a key factor underlying holistic face processing. - Cheston Tan; Tomaso A. Poggio (CoRR, 2014)

Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5). - Pierre Baldi; Kenji Fukumizu; Tomaso A. Poggio (NII Shonan Meet. Rep., 2014)

Machine Recognition of Objects. - Tomaso A. Poggio; Shimon Ullman (Computer Vision, A Reference Guide, 2014)

Visual Cortex Models for Object Recognition. - Tomaso A. Poggio; Shimon Ullman (Computer Vision, A Reference Guide, 2014)

Convex Learning of Multiple Tasks and their Structure. - Carlo Ciliberto; Youssef Mroueh; Tomaso A. Poggio; Lorenzo Rosasco (ICML, 2015)

Discriminative template learning in group-convolutional networks for invariant speech representations. - Chiyuan Zhang; Stephen Voinea; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (INTERSPEECH, 2015)

Learning with a Wasserstein Loss. - Charlie Frogner; Chiyuan Zhang; Hossein Mobahi; Mauricio Araya-Polo; Tomaso A. Poggio (NIPS, 2015)

Learning with Group Invariant Features: A Kernel Perspective. - Youssef Mroueh; Stephen Voinea; Tomaso A. Poggio (NIPS, 2015)

On Invariance and Selectivity in Representation Learning. - Fabio Anselmi; Lorenzo Rosasco; Tomaso A. Poggio (CoRR, 2015)

Deep Convolutional Networks are Hierarchical Kernel Machines. - Fabio Anselmi; Lorenzo Rosasco; Cheston Tan; Tomaso A. Poggio (CoRR, 2015)

Foveation-based Mechanisms Alleviate Adversarial Examples. - Yan Luo 002; Xavier Boix; Gemma Roig; Tomaso A. Poggio; Qi Zhao 001 (CoRR, 2015)

The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. - Joel Z. Leibo; Qianli Liao; Fabio Anselmi; Tomaso A. Poggio (PLoS Comput. Biol., 2015)

How Important Is Weight Symmetry in Backpropagation? - Qianli Liao; Joel Z. Leibo; Tomaso A. Poggio (AAAI, 2016)

Holographic Embeddings of Knowledge Graphs. - Maximilian Nickel; Lorenzo Rosasco; Tomaso A. Poggio (AAAI, 2016)

Turing++ Questions: A Test for the Science of (Human) Intelligence. - Tomaso A. Poggio; Ethan Meyers (AI Mag., 2016)

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. - Joel Z. Leibo; Qianli Liao; Winrich Freiwald; Fabio Anselmi; Tomaso A. Poggio (CoRR, 2016)

Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. - Qianli Liao; Kenji Kawaguchi; Tomaso A. Poggio (CoRR, 2016)

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. - Qianli Liao; Tomaso A. Poggio (CoRR, 2016)

Learning Real and Boolean Functions: When Is Deep Better Than Shallow. - Hrushikesh N. Mhaskar; Qianli Liao; Tomaso A. Poggio (CoRR, 2016)

Deep vs. shallow networks : An approximation theory perspective. - Hrushikesh N. Mhaskar; Tomaso A. Poggio (CoRR, 2016)

Unsupervised learning of invariant representations. - Fabio Anselmi; Joel Z. Leibo; Lorenzo Rosasco; Jim Mutch; Andrea Tacchetti; Tomaso A. Poggio (Theor. Comput. Sci., 2016)

When and Why Are Deep Networks Better Than Shallow Ones? - Hrushikesh N. Mhaskar; Qianli Liao; Tomaso A. Poggio (AAAI, 2017)

Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. - Francis X. Chen; Gemma Roig; Leyla Isik; Xavier Boix; Tomaso A. Poggio (AAAI Spring Symposia, 2017)

Is the Human Visual System Invariant to Translation and Scale? - Yena Han; Gemma Roig; Gadi Geiger; Tomaso A. Poggio (AAAI Spring Symposia, 2017)

Group Invariant Deep Representations for Image Instance Retrieval. - Olivier Morère; Antoine Veillard; Jie Lin 001; Julie Petta; Vijay Chandrasekhar 001; Tomaso A. Poggio (AAAI Spring Symposia, 2017)

Representation Learning from Orbit Sets for One-Shot Classification. - Andrea Tacchetti; Stephen Voinea; Georgios Evangelopoulos; Tomaso A. Poggio (AAAI Spring Symposia, 2017)

Compression of Deep Neural Networks for Image Instance Retrieval. - Vijay Chandrasekhar 001; Jie Lin 001; Qianli Liao; Olivier Morère; Antoine Veillard; Ling-Yu Duan; Tomaso A. Poggio (DCC, 2017)

Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. - Olivier Morère; Jie Lin 001; Antoine Veillard; Ling-Yu Duan; Vijay Chandrasekhar 001; Tomaso A. Poggio (ICMR, 2017)

Do Deep Neural Networks Suffer from Crowding? - Anna Volokitin; Gemma Roig; Tomaso A. Poggio (NIPS, 2017)

Pruning Convolutional Neural Networks for Image Instance Retrieval. - Gaurav Manek; Jie Lin 001; Vijay Chandrasekhar 001; Lingyu Duan; Sateesh Giduthuri; Xiaoli Li 001; Tomaso A. Poggio (CoRR, 2017)

Theory II: Landscape of the Empirical Risk in Deep Learning. - Tomaso A. Poggio; Qianli Liao (CoRR, 2017)

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. - Tomaso A. Poggio; Hrushikesh N. Mhaskar; Lorenzo Rosasco; Brando Miranda; Qianli Liao (Int. J. Autom. Comput., 2017)

Invariant recognition drives neural representations of action sequences. - Andrea Tacchetti; Leyla Isik; Tomaso A. Poggio (PLoS Comput. Biol., 2017)

Theory of Deep Learning III: explaining the non-overfitting puzzle. - Tomaso A. Poggio; Kenji Kawaguchi; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Xavier Boix; Jack Hidary; Hrushikesh N. Mhaskar (CoRR, 2018)

Theory of Deep Learning IIb: Optimization Properties of SGD. - Chiyuan Zhang; Qianli Liao; Alexander Rakhlin; Brando Miranda; Noah Golowich; Tomaso A. Poggio (CoRR, 2018)

Theory IIIb: Generalization in Deep Networks. - Tomaso A. Poggio; Qianli Liao; Brando Miranda; Andrzej Banburski; Xavier Boix; Jack Hidary (CoRR, 2018)

A Surprising Linear Relationship Predicts Test Performance in Deep Networks. - Qianli Liao; Brando Miranda; Andrzej Banburski; Jack Hidary; Tomaso A. Poggio (CoRR, 2018)

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. - Tengyuan Liang; Tomaso A. Poggio; Alexander Rakhlin; James Stokes (AISTATS, 2019)

Biologically-Plausible Learning Algorithms Can Scale to Large Datasets. - Will Xiao; Honglin Chen; Qianli Liao; Tomaso A. Poggio (ICLR (Poster), 2019)

Fast and Flexible Inference of Joint Distributions from their Marginals. - Charlie Frogner; Tomaso A. Poggio (ICML, 2019)

Theory III: Dynamics and Generalization in Deep Networks. - Andrzej Banburski; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Bob Liang; Jack Hidary; Tomaso A. Poggio (CoRR, 2019)

Function approximation by deep networks. - Hrushikesh N. Mhaskar; Tomaso A. Poggio (CoRR, 2019)

Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization. - Tomaso A. Poggio; Andrzej Banburski; Qianli Liao (CoRR, 2019)

Double descent in the condition number. - Tomaso A. Poggio; Gil Kur; Andrzej Banburski (CoRR, 2019)

Symmetry-adapted representation learning. - Fabio Anselmi; Georgios Evangelopoulos; Lorenzo Rosasco; Tomaso A. Poggio (Pattern Recognit., 2019)

Approximate Inference with Wasserstein Gradient Flows. - Charlie Frogner; Tomaso A. Poggio (AISTATS, 2020)

Cross-Domain Adversarial Reprogramming of a Recurrent Neural Network. - Alexandra Proca; Andrzej Banburski; Tomaso A. Poggio (CogSci, 2020)

Biologically Inspired Mechanisms for Adversarial Robustness. - Manish V. Reddy; Andrzej Banburski; Nishka Pant; Tomaso A. Poggio (NeurIPS, 2020)

Hierarchically Local Tasks and Deep Convolutional Networks. - Arturo Deza; Qianli Liao; Andrzej Banburski; Tomaso A. Poggio (CoRR, 2020)

For interpolating kernel machines, the minimum norm ERM solution is the most stable. - Akshay Rangamani; Lorenzo Rosasco; Tomaso A. Poggio (CoRR, 2020)

CUDA-Optimized real-time rendering of a Foveated Visual System. - Elian Malkin; Arturo Deza; Tomaso A. Poggio (CoRR, 2020)

An analysis of training and generalization errors in shallow and deep networks. - Hrushikesh N. Mhaskar; Tomaso A. Poggio (Neural Networks, 2020)

Neural-Guided, Bidirectional Program Search for Abstraction and Reasoning. - Simon Alford; Anshula Gandhi; Akshay Rangamani; Andrzej Banburski; Tony Wang; Sylee Dandekar; John Chin; Tomaso A. Poggio; Peter Chin 001 (COMPLEX NETWORKS, 2021)

Explicit regularization and implicit bias in deep network classifiers trained with the square loss. - Tomaso A. Poggio; Qianli Liao (CoRR, 2021)

The Effects of Image Distribution and Task on Adversarial Robustness. - Owen Kunhardt; Arturo Deza; Tomaso A. Poggio (CoRR, 2021)

Distribution of Classification Margins: Are All Data Equal? - Andrzej Banburski; Fernanda De La Torre; Nishka Pant; Ishana Shastri; Tomaso A. Poggio (CoRR, 2021)

Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows. - Amir Adler; Mauricio Araya-Polo; Tomaso A. Poggio (IEEE Signal Process. Mag., 2021)

Achieving Adversarial Robustness in Deep Learning-Based Overhead Imaging. - Dagen Braun; Matthew D. Reisman; Larry Dewell; Andrzej Banburski-Fahey; Arturo Deza; Tomaso A. Poggio (AIPR, 2022)

Representation Learning in Sensory Cortex: A Theory. - Fabio Anselmi; Tomaso A. Poggio (IEEE Access, 2022)

SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks. - Tomer Galanti; Tomaso A. Poggio (CoRR, 2022)

Iterative regularization in classification via hinge loss diagonal descent. - Vassilis Apidopoulos; Tomaso A. Poggio; Lorenzo Rosasco; Silvia Villa (CoRR, 2022)

System Identification of Neural Systems: If We Got It Right, Would We Know? - Yena Han; Tomaso A. Poggio; Brian Cheung (ICML, 2023)

Feature learning in deep classifiers through Intermediate Neural Collapse. - Akshay Rangamani; Marius Lindegaard; Tomer Galanti; Tomaso A. Poggio (ICML, 2023)

Norm-based Generalization Bounds for Sparse Neural Networks. - Tomer Galanti; Mengjia Xu; Liane Galanti; Tomaso A. Poggio (NeurIPS, 2023)

Norm-based Generalization Bounds for Compositionally Sparse Neural Networks. - Tomer Galanti; Mengjia Xu; Liane Galanti; Tomaso A. Poggio (CoRR, 2023)

How to guess a gradient. - Utkarsh Singhal; Brian Cheung; Kartik Chandra; Jonathan Ragan-Kelley; Joshua B. Tenenbaum; Tomaso A. Poggio; Stella X. Yu (CoRR, 2023)

On the Power of Decision Trees in Auto-Regressive Language Modeling. - Yulu Gan; Tomer Galanti; Tomaso A. Poggio; Eran Malach (NeurIPS, 2024)

How Neural Networks Learn the Support is an Implicit Regularization Effect of SGD. - Pierfrancesco Beneventano; Andrea Pinto; Tomaso A. Poggio (CoRR, 2024)

Training the Untrainable: Introducing Inductive Bias via Representational Alignment. - Vighnesh Subramaniam; David Mayo; Colin Conwell; Tomaso A. Poggio; Boris Katz; Brian Cheung; Andrei Barbu (CoRR, 2024)

Self-Assembly of a Biologically Plausible Learning Circuit. - Qianli Liao; Liu Ziyin 001; Yulu Gan; Brian Cheung; Mark Harnett; Tomaso A. Poggio (CoRR, 2024)

On Generalization Bounds for Neural Networks with Low Rank Layers. - Andrea Pinto; Akshay Rangamani; Tomaso A. Poggio (ALT, 2025)

SGD with Weight Decay Secretly Minimizes the Ranks of Your Neural Networks. - Tomer Galanti; Zachary S. Siegel; Aparna Gupte; Tomaso A. Poggio (CPAL, 2025)

Formation of Representations in Neural Networks. - Liu Ziyin 001; Isaac L. Chuang; Tomer Galanti; Tomaso A. Poggio (ICLR, 2025)

What if Eye...? Computationally Recreating Vision Evolution. - Kushagra Tiwary; Aaron Young; Zaid Tasneem; Tzofi Klinghoffer; Akshat Dave; Tomaso A. Poggio; Dan Nilsson; Brian Cheung; Ramesh Raskar (CoRR, 2025)

Parameter Symmetry Breaking and Restoration Determines the Hierarchical Learning in AI Systems. - Liu Ziyin 001; Yizhou Xu; Tomaso A. Poggio; Isaac L. Chuang (CoRR, 2025)

Heterosynaptic Circuits Are Universal Gradient Machines. - Liu Ziyin 001; Isaac L. Chuang; Tomaso A. Poggio (CoRR, 2025)

Emergence of Hebbian Dynamics in Regularized Non-Local Learners. - David Koplow; Tomaso A. Poggio; Liu Ziyin 001 (CoRR, 2025)

Position: A Theory of Deep Learning Must Include Compositional Sparsity. - David A. Danhofer; Davide D'Ascenzo; Rafael Dubach; Tomaso A. Poggio (CoRR, 2025)