Tomaso A. Poggio

发表

Visual Speech Synthesis by Morphing Visemes

Tony Ezzat, Tomaso A. Poggio, T. Poggio, 2000, International Journal of Computer Vision.

A bootstrapping algorithm for learning linear models of object classes

Tomaso A. Poggio, Thomas Vetter, Michael J. Jones, 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Regularization Theory and Neural Networks Architectures

Tomaso A. Poggio, Michael J. Jones, Federico Girosi, 1995, Neural Computation.

Face Recognition through Geometrical Features

Roberto Brunelli, Tomaso A. Poggio, T. Poggio, 1992, ECCV.

The Computational Magic of Pattern Recognition in Cortex: A Theory of Selectivity and Invariance

Tomaso A. Poggio, T. Poggio, 2013, ICPRAM.

Motion Field and Optical Flow: Qualitative Properties

Tomaso A. Poggio, Alessandro Verri, A. Verri, 1989, IEEE Trans. Pattern Anal. Mach. Intell..

How Important Is Weight Symmetry in Backpropagation? pdf

Joel Z. Leibo, Tomaso A. Poggio, Qianli Liao, 2015, AAAI.

Learning and Perceptual Interfaces

Tomaso A. Poggio, 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

Theory IIIb: Generalization in Deep Networks pdf

Tomaso A. Poggio, Brando Miranda, Qianli Liao, 2018, ArXiv.

Max-Gabor analysis and synthesis of spectrograms

Tony Ezzat, Tomaso A. Poggio, Jake V. Bouvrie, 2006, INTERSPEECH.

MikeTalk: a talking facial display based on morphing visemes

Tony Ezzat, Tomaso A. Poggio, T. Poggio, 1998, Proceedings Computer Animation '98 (Cat. No.98EX169).

Theory II: Landscape of the Empirical Risk in Deep Learning pdf

Tomaso A. Poggio, Qianli Liao, T. Poggio, 2017, ArXiv.

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review

Lorenzo Rosasco, Tomaso A. Poggio, Brando Miranda, 2016, International Journal of Automation and Computing.

Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5)

Kenji Fukumizu, Pierre Baldi, Tomaso A. Poggio, 2014, NII Shonan Meet. Rep..

Is the Human Visual System Invariant to Translation and Scale?

Tomaso A. Poggio, Gemma Roig, Yena Han, 2017, AAAI Spring Symposia.

Pedestrian detection using wavelet templates

Tomaso A. Poggio, Pawan Sinha, Michael Oren, 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Theory of Deep Learning III: explaining the non-overfitting puzzle pdf

Lorenzo Rosasco, Tomaso A. Poggio, Kenji Kawaguchi, 2017, ArXiv.

A pattern classification approach to dynamical object detection

Tomaso A. Poggio, Constantine Papageorgiou, T. Poggio, 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

Regularization Networks and Support Vector Machines

Tomaso A. Poggio, Massimiliano Pontil, Theodoros Evgeniou, 2000, Adv. Comput. Math..

Computational role of eccentricity dependent cortical magnification pdf

Tomaso A. Poggio, Jim Mutch, Leyla Isik, 2014, ArXiv.

People recognition and pose estimation in image sequences

Tomaso A. Poggio, Massimiliano Pontil, Chikahito Nakajima, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Turing++ Questions: A Test for the Science of (Human) Intelligence

Tomaso A. Poggio, Ethan Meyers, Ethan M. Meyers, 2016, AI Mag..

Videorealistic talking faces: a morphing approach

Tony Ezzat, Tomaso A. Poggio, T. Poggio, 1997, AVSP.

Automatic person recognition by acoustic and geometric features

Roberto Brunelli, Tomaso A. Poggio, Daniele Falavigna, 1995, Machine Vision and Applications.

A Component-based Framework for Face Detection and Identification

Thomas Serre, Tomaso A. Poggio, Bernd Heisele, 2007, International Journal of Computer Vision.

Model-based matching of line drawings by linear combinations of prototypes

Tomaso A. Poggio, Michael J. Jones, T. Poggio, 1995, Proceedings of IEEE International Conference on Computer Vision.

Learning Human Face Detection in Cluttered Scenes

Tomaso A. Poggio, Kah Kay Sung, T. Poggio, 1995, CAIP.

Learning Features of Intermediate Complexity for the Recognition of Biological Motion

Martin A. Giese, Thomas Serre, Tomaso A. Poggio, 2005, ICANN.

9.913-C Pattern Recognition for Machine Vision, Spring 2002

Tomaso A. Poggio, Yuri A. Ivanov, Bernd Heisele, 2002 .

A Biologically Inspired System for Action Recognition

Thomas Serre, Lior Wolf, Tomaso A. Poggio, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Hierarchical morphable models

Tomaso A. Poggio, Michael J. Jones, 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

Deep Convolutional Networks are Hierarchical Kernel Machines pdf

Lorenzo Rosasco, Tomaso A. Poggio, Cheston Tan, 2015, ArXiv.

Representation Learning from Orbit Sets for One-Shot Classification

Tomaso A. Poggio, Andrea Tacchetti, Stephen Voinea, 2017, AAAI Spring Symposia.

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks

Tomaso A. Poggio, Tengyuan Liang, Alexander Rakhlin, 2017, AISTATS.

Feature reduction and hierarchy of classifiers for fast object detection in video images

Sayan Mukherjee, Thomas Serre, Tomaso A. Poggio, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

An Optimal Scale for Edge Detection

Tomaso A. Poggio, Davi Geiger, T. Poggio, 1988, IJCAI.

Fast and Flexible Inference of Joint Distributions from their Marginals

Tomaso A. Poggio, Charlie Frogner, T. Poggio, 2019, ICML.

Image Synthesis from a Single Example Image

Tomaso A. Poggio, Thomas Vetter, T. Poggio, 1996, ECCV.

Trainable, vision-based automated home cage behavioral phenotyping

Andrew Steele, Thomas Serre, Tomaso A. Poggio, 2010, MB '10.

Neural tuning size is a key factor underlying holistic face processing pdf

Tomaso A. Poggio, Cheston Tan, T. Poggio, 2014, ArXiv.

On Invariance in Hierarchical Models

Lorenzo Rosasco, Tomaso A. Poggio, Jake V. Bouvrie, 2009, NIPS.

Invariant recognition drives neural representations of action sequences

Tomaso A. Poggio, Andrea Tacchetti, Leyla Isik, 2017, PLoS Comput. Biol..

When and Why Are Deep Networks Better Than Shallow Ones?

Tomaso A. Poggio, Qianli Liao, Hrushikesh Mhaskar, 2017, AAAI.

Unsupervised learning of clutter-resistant visual representations from natural videos pdf

Joel Z. Leibo, Tomaso A. Poggio, Qianli Liao, 2014, ArXiv.

On parallel stereo

Tomaso A. Poggio, Michael Drumheller, T. Poggio, 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

Throwing Down the Visual Intelligence Gauntlet

Joel Z. Leibo, Tomaso A. Poggio, Cheston Tan, 2013, Machine Learning for Computer Vision.

Convex Learning of Multiple Tasks and their Structure

Lorenzo Rosasco, Tomaso A. Poggio, Carlo Ciliberto, 2015, ICML.

Analog VLSI systems for image acquisition and fast early vision processing

Hae-Seung Lee, Tomaso A. Poggio, John L. Wyatt, 1992, International Journal of Computer Vision.

Fingerprints Theorems

Alan L. Yuille, Tomaso A. Poggio, T. Poggio, 1984, AAAI.

GEM: A Global Electronic Market System

Israel Ben-Shaul, Tomaso A. Poggio, Andrew W. Lo, 1999, Inf. Syst..

On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision

Thomas Serre, Tomaso A. Poggio, Maximilian Riesenhuber, 2002, Biologically Motivated Computer Vision.

Machine Learning, Machine Vision, and the Brain

Tomaso A. Poggio, Christian R. Shelton, T. Poggio, 1999, AI Mag..

Morphing spectral envelopes using audio flow

James R. Glass, Tony Ezzat, Tomaso A. Poggio, 2005, INTERSPEECH.

Face recognition with support vector machines: global versus component-based approach

Tomaso A. Poggio, Purdy Ho, Bernd Heisele, 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

Group Invariant Deep Representations for Image Instance Retrieval pdf

Jie Lin, Tomaso A. Poggio, Vijay Chandrasekhar, 2016, AAAI Spring Symposia.

Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? pdf

Joel Z. Leibo, Tomaso A. Poggio, Youssef Mroueh, 2013, ArXiv.

Parallel Optical Flow Using Local Voting

James J. Little, Heinrich H. Bülthoff, Tomaso A. Poggio, 1988, [1988 Proceedings] Second International Conference on Computer Vision.

Learning An Invariant Speech Representation pdf

Lorenzo Rosasco, Tomaso A. Poggio, Stephen Voinea, 2014, ArXiv.

Representation Properties of Networks: Kolmogorov's Theorem Is Irrelevant

Tomaso A. Poggio, Federico Girosi, F. Girosi, 1989, Neural Computation.

Support Vector Machine Classification of Microarray Data

Jill P. Mesirov, Donna K. Slonim, Tomaso A. Poggio, 2001 .

Pruning Convolutional Neural Networks for Image Instance Retrieval pdf

Ling-Yu Duan, Jie Lin, Tomaso A. Poggio, 2017, ArXiv.

Neural representation of action sequences: how far can a simple snippet-matching model take us?

Thomas Serre, Tomaso A. Poggio, Cheston Tan, 2013, NIPS.

A multiview face identification model with no geometric constraints

Tomaso A. Poggio, Jerry Jun Yokono, T. Poggio, 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

Example-Based Object Detection in Images by Components

Tomaso A. Poggio, Constantine Papageorgiou, Anuj Mohan, 2001, IEEE Trans. Pattern Anal. Mach. Intell..

Face Recognition: Features Versus Templates

Roberto Brunelli, Tomaso A. Poggio, T. Poggio, 1993, IEEE Trans. Pattern Anal. Mach. Intell..

Machine Recognition of Objects

Shimon Ullman, Tomaso A. Poggio, T. Poggio, 2014, Computer Vision, A Reference Guide.

Example-Based Learning for View-Based Human Face Detection

Tomaso A. Poggio, Kah Kay Sung, T. Poggio, 1998, IEEE Trans. Pattern Anal. Mach. Intell..

Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization

Sayan Mukherjee, Tomaso A. Poggio, Partha Niyogi, 2006, Adv. Comput. Math..

Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization pdf

Tomaso A. Poggio, Qianli Liao, Andrzej Banburski, 2019, ArXiv.

A deep representation for invariance and music classification

Lorenzo Rosasco, Tomaso A. Poggio, Stephen Voinea, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Categorization by Learning and Combining Object Parts

Thomas Serre, Tomaso A. Poggio, Massimiliano Pontil, 2001, NIPS.

Symmetry-adapted representation learning

Lorenzo Rosasco, Tomaso A. Poggio, Georgios Evangelopoulos, 2019, Pattern Recognit..

A Trainable System for Object Detection

Tomaso A. Poggio, Constantine Papageorgiou, T. Poggio, 2000, International Journal of Computer Vision.

Integration of vision modules and labeling of surface discontinuities

Daphna Weinshall, Tomaso A. Poggio, Davi Geiger, 1989, IEEE Trans. Syst. Man Cybern..

Introduction: Learning and Vision at CBCL

Tomaso A. Poggio, Alessandro Verri, 2004, International Journal of Computer Vision.

Phone classification by a hierarchy of invariant representation layers

Lorenzo Rosasco, Tomaso A. Poggio, Stephen Voinea, 2014, INTERSPEECH.

Biophysical Models of Neural Computation: Max and Tuning Circuits

Tomaso A. Poggio, Ulf Knoblich, Jake V. Bouvrie, 2006, WImBI.

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex pdf

Tomaso A. Poggio, Qianli Liao, T. Poggio, 2016, ArXiv.

Word-level invariant representations from acoustic waveforms

Lorenzo Rosasco, Tomaso A. Poggio, Stephen Voinea, 2014, INTERSPEECH.

Attentional Selection for Object Recognition - A Gentle Way

Christof Koch, Laurent Itti, Tomaso A. Poggio, 2002, Biologically Motivated Computer Vision.

A neuromorphic approach to computer vision

Thomas Serre, Tomaso A. Poggio, T. Poggio, 2010, Commun. ACM.

Image-based view synthesis by combining trilinear tensors and learning techniques

Amnon Shashua, Tomaso A. Poggio, Shai Avidan, 1997, VRST '97.

Learning a Color Algorithm from Examples

Tomaso A. Poggio, Anya C. Hurlbert, T. Poggio, 1987, NIPS.

Feature Selection for SVMs

Sayan Mukherjee, Jason Weston, Tomaso A. Poggio, 2000, NIPS.

Learning invariant representations and applications to face verification

Joel Z. Leibo, Tomaso A. Poggio, Qianli Liao, 2013, NIPS.

3D Object Recognition: A Model of View-Tuned Neurons

Tomaso A. Poggio, Nikos K. Logothetis, Emanuela Bricolo, 1996, NIPS.

Scaling Theorems for Zero Crossings

Alan L. Yuille, Tomaso A. Poggio, T. Poggio, 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

CBF: A New Framework for Object Categorization in Cortex

Tomaso A. Poggio, Maximilian Riesenhuber, T. Poggio, 2000, Biologically Motivated Computer Vision.

The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex

Joel Z. Leibo, Tomaso A. Poggio, Qianli Liao, 2014, bioRxiv.

Reanimating Faces in Images and Video

Tomaso A. Poggio, Thomas Vetter, Volker Blanz, 2003, Comput. Graph. Forum.

Online Learning, Stability, and Stochastic Gradient Descent pdf

Lorenzo Rosasco, Tomaso A. Poggio, Stephen Voinea, 2011, ArXiv.

Learning and disrupting invariance in visual recognition with a temporal association rule

Joel Z. Leibo, Tomaso A. Poggio, Leyla Isik, 2011, Front. Comput. Neurosci..

Recognizing Expressions by Direct Estimation of the Parameters of a Pixel Morphable Model

Tomaso A. Poggio, Vinay P. Kumar, T. Poggio, 2002, Biologically Motivated Computer Vision.

Face recognition from one example view

David Beymer, Tomaso A. Poggio, T. Poggio, 1995, Proceedings of IEEE International Conference on Computer Vision.

On Learnability, Complexity and Stability pdf

Lorenzo Rosasco, Tomaso A. Poggio, Silvia Villa, 2013, Empirical Inference.

Learning Manifolds with K-Means and K-Flats

Lorenzo Rosasco, Tomaso A. Poggio, Guillermo D. Cañas, 2012, NIPS.

A regularized solution to edge detection

Alan L. Yuille, Tomaso A. Poggio, Harry Voorhees, 1985, J. Complex..

HyperBF Networks for Real Object Recognition

Roberto Brunelli, Tomaso A. Poggio, T. Poggio, 1991, IJCAI.

Learning with Group Invariant Features: A Kernel Perspective

Tomaso A. Poggio, Youssef Mroueh, Stephen Voinea, 2015, NIPS.

Unsupervised learning of invariant representations

Lorenzo Rosasco, Joel Z. Leibo, Tomaso A. Poggio, 2016, Theor. Comput. Sci..

Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? pdf

Lorenzo Rosasco, Joel Z. Leibo, Tomaso A. Poggio, 2014 .

A Network for Image Segmentation Using Color

Tomaso A. Poggio, Anya C. Hurlbert, T. Poggio, 1988, NIPS.

Facial analysis and synthesis using image-based models

Tony Ezzat, Tomaso A. Poggio, T. Poggio, 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

Statistical Learning Theory: A Primer

Tomaso A. Poggio, Massimiliano Pontil, Theodoros Evgeniou, 2000, International Journal of Computer Vision.

Guest Editors' Introduction: Neural Networks For Signal Processing

Jenq-Neng Hwang, Sun-Yuan Kung, Shigeru Katagiri, 1997, IEEE Trans. Signal Process..

View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation

Joel Z. Leibo, Tomaso A. Poggio, Qianli Liao, 2016, Current Biology.

Object recognition with features inspired by visual cortex

Thomas Serre, Lior Wolf, Tomaso A. Poggio, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Learning with a Wasserstein Loss

Hossein Mobahi, Tomaso A. Poggio, Mauricio Araya-Polo, 2015, NIPS.

Multidimensional morphable models

Tomaso A. Poggio, Michael J. Jones, T. Poggio, 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

On Edge Detection

Tomaso A. Poggio, Vincent Torre, V. Torre, 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning Real and Boolean Functions: When Is Deep Better Than Shallow pdf

Tomaso A. Poggio, Qianli Liao, Hrushikesh Mhaskar, 2016, ArXiv.

Early vision: From computational structure to algorithms and parallel hardware

Tomaso A. Poggio, 1985, Comput. Vis. Graph. Image Process..

Finding Human Faces with a Gaussian Mixture Distribution-Based Face Model

Tomaso A. Poggio, Kah Kay Sung, T. Poggio, 1995, ACCV.

Component-based face detection

Thomas Serre, Tomaso A. Poggio, Massimiliano Pontil, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

On Invariance and Selectivity in Representation Learning pdf

Lorenzo Rosasco, Tomaso A. Poggio, Fabio Anselmi, 2015, ArXiv.

Comparing support vector machines with Gaussian kernels to radial basis function classifiers

Bernhard Schölkopf, Tomaso A. Poggio, Federico Girosi, 1997, IEEE Trans. Signal Process..

An Analytical Method for Multiclass Molecular Cancer Classification

Sayan Mukherjee, Jill P. Mesirov, Sridhar Ramaswamy, 2003, SIAM Rev..

Face recognition: component-based versus global approaches

Tomaso A. Poggio, Purdy Ho, Bernd Heisele, 2003, Comput. Vis. Image Underst..

Parallel processing in the DARPA strategic computing vision program

John R. Kender, Tomaso A. Poggio, Charles C. Weems, 1991, IEEE Expert.

A computational model of visuomotor development

Tomaso A. Poggio, Anthony Passera, 1993 .

Learning-based approach to real time tracking and analysis of faces

Tomaso A. Poggio, Vinay P. Kumar, T. Poggio, 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

Learning of visual modules from examples: A framework for understanding adaptive visual performance

Tomaso A. Poggio, Shimon Edelman, Manfred Fahle, 1992, CVGIP Image Underst..

Oriented filters for object recognition: an empirical study

Tomaso A. Poggio, Jerry Jun Yokono, 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

Why The Brain Separates Face Recognition From Object Recognition

Joel Z. Leibo, Tomaso A. Poggio, Jim Mutch, 2011, NIPS.

Do Deep Neural Networks Suffer from Crowding?

Tomaso A. Poggio, Gemma Roig, Anna Volokitin, 2017, NIPS.

Bounds on the Generalization Performance of Kernel Machine Ensembles

Tomaso A. Poggio, Massimiliano Pontil, Theodoros Evgeniou, 2000, ICML.

Approximate inference with Wasserstein gradient flows

Tomaso A. Poggio, Charlie Frogner, T. Poggio, 2018, AISTATS.

Theory of Deep Learning IIb: Optimization Properties of SGD pdf

Tomaso A. Poggio, Noah Golowich, Brando Miranda, 2018, ArXiv.

Optical flow from 1-D correlation: Application to a simple time-to-crash detector

Nicola Ancona, Tomaso A. Poggio, T. Poggio, 1993, 1993 (4th) International Conference on Computer Vision.

Learning, regularization and splines

Tomaso A. Poggio, T. Poggio, 1988, Neural Networks.

Sparse correlation kernel reconstruction

Tomaso A. Poggio, Federico Girosi, Constantine Papageorgiou, 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

Hierarchical Learning Machines and Neuroscience of Visual Cortex

Tomaso A. Poggio, 2010, ECML/PKDD.

ScalingTheorems forZero Crossings

Tomaso A. Poggio, T. Poggio, 1986 .

Multiclass Learning with Simplex Coding

Lorenzo Rosasco, Tomaso A. Poggio, Jean-Jacques E. Slotine, 2012, NIPS.

Full-body person recognition system

Tomaso A. Poggio, Massimiliano Pontil, Bernd Heisele, 2003, Pattern Recognit..

Subtasks of Unconstrained Face Recognition

Joel Z. Leibo, Tomaso A. Poggio, Qianli Liao, 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

Report on Workshop on High Performance Computing and Communications for Grand Challenge Applications: Computer Vision, Speech and Natural Language Processing, and Artificial Intelligence

Salvatore J. Stolfo, Sergei Nirenburg, Richard E. Korf, 1993, IEEE Trans. Knowl. Data Eng..

Werner Reichardt 1924–1992

Tomaso A. Poggio, Tomaso A. Poggio, 1993, Biological Cybernetics.

Holographic Embeddings of Knowledge Graphs

Lorenzo Rosasco, Tomaso A. Poggio, Maximilian Nickel, 2015, AAAI.

Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning pdf

Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, 2016, ArXiv.

Invariant representation for blur and down-sampling transformations

Chunhong Pan, Joel Z. Leibo, Tomaso A. Poggio, 2016, 2016 IEEE International Conference on Image Processing (ICIP).

Visual Categorization: How the Monkey Brain Does It

David J. Freedman, Tomaso A. Poggio, Earl K. Miller, 2002, Biologically Motivated Computer Vision.

Representation properties of multilayer feedforward networks

Tomaso A. Poggio, B. Moore, T. Poggio, 1988, Neural Networks.

Models of visual cortex

Thomas Serre, Tomaso A. Poggio, T. Poggio, 2013, Scholarpedia.

Foveation-based Mechanisms Alleviate Adversarial Examples pdf

Qi Zhao, Yan Luo, Tomaso A. Poggio, 2015, ArXiv.

Localized spectro-temporal cepstral analysis of speech

Tony Ezzat, Tomaso A. Poggio, Jake V. Bouvrie, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

Morphable Models for the Analysis and Synthesis of Complex Motion Patterns

Martin A. Giese, Tomaso A. Poggio, T. Poggio, 2000, International Journal of Computer Vision.

Trainable pedestrian detection

Tomaso A. Poggio, Constantine Papageorgiou, T. Poggio, 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

Publisher Accessed Terms of Use Detailed Terms Mathematics of the Neural Response

Lorenzo Rosasco, Tomaso A. Poggio, Stephen Smale, 2022 .

MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES

Roberto Brunelli, Tomaso A. Poggio, T. Poggio, 2001 .

Incremental and Decremental Support Vector Machine Learning

Gert Cauwenberghs, Tomaso A. Poggio, T. Poggio, 2000, NIPS.

An analysis of training and generalization errors in shallow and deep networks

Tomaso A. Poggio, Hrushikesh Mhaskar, T. Poggio, 2018, Neural Networks.

Linear Object Classes and Image Synthesis From a Single Example Image

Tomaso A. Poggio, Thomas Vetter, T. Poggio, 1997, IEEE Trans. Pattern Anal. Mach. Intell..

AM-FM Demodulation of Spectrograms using Localized 2D Max-Gabor Analysis

Tony Ezzat, Tomaso A. Poggio, Jake V. Bouvrie, 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

Optical flow from 1-D correlation: Application to a simple time-to-crash detector

Nicola Ancona, Tomaso A. Poggio, 2005, International Journal of Computer Vision.

Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision

Tomaso A. Poggio, Gemma Roig, Leyla Isik, 2017, AAAI Spring Symposia.

Extensions of a Theory of Networks for Approximation and Learning

Tomaso A. Poggio, Federico Girosi, Bruno Caprile, 1990, NIPS.

Image representations for object detection using kernel classifiers

Tomaso A. Poggio, Massimiliano Pontil, C. Papageorgiou, 2000 .

A general framework for object detection

Tomaso A. Poggio, Michael Oren, Constantine Papageorgiou, 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

Biologically-plausible learning algorithms can scale to large datasets pdf

Tomaso A. Poggio, Qianli Liao, Honglin Chen, 2018, ICLR.

Discriminative word-spotting using ordered spectro-temporal patch features

Tony Ezzat, Tomaso A. Poggio, T. Poggio, 2008, SAPA@INTERSPEECH.

A Canonical Neural Circuit for Cortical Nonlinear Operations

Tomaso A. Poggio, Minjoon Kouh, T. Poggio, 2008, Neural Computation.

Object recognition and detection by a combination of support vector machine and rotation invariant phase only correlation

Tomaso A. Poggio, Massimiliano Pontil, Norihiko Itoh, 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

Neuroscience: New Insights for AI?

Tomaso A. Poggio, 2006, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

Caricatural effects in automated face perception

Roberto Brunelli, Tomaso A. Poggio, T. Poggio, 1993, Biological Cybernetics.

A Surprising Linear Relationship Predicts Test Performance in Deep Networks pdf

Tomaso A. Poggio, Brando Miranda, Qianli Liao, 2018, ArXiv.

Introduction to the Special Section on Learning in Computer Vision

Bir Bhanu, Tomaso A. Poggio, T. Poggio, 1994, IEEE Trans. Pattern Anal. Mach. Intell..

A Sparse Representation for Function Approximation

Tomaso A. Poggio, Federico Girosi, F. Girosi, 1998, Neural Computation.

Hierarchical classification and feature reduction for fast face detection with support vector machines

Thomas Serre, Tomaso A. Poggio, Bernd Heisele, 2003, Pattern Recognit..

Just One View: Invariances in Inferotemporal Cell Tuning

Tomaso A. Poggio, Maximilian Riesenhuber, T. Poggio, 1997, NIPS.

Compression of Deep Neural Networks for Image Instance Retrieval

Ling-Yu Duan, Jie Lin, Tomaso A. Poggio, 2017, 2017 Data Compression Conference (DCC).

Visual Cortex Models for Object Recognition

Shimon Ullman, Tomaso A. Poggio, T. Poggio, 2014, Computer Vision, A Reference Guide.

Guest Editorial Applications Of Artificial Neural Networks To Image Processing

Aggelos K. Katsaggelos, Rama Chellappa, Sun-Yuan Kung, 1998, IEEE Trans. Image Process..

Theory III: Dynamics and Generalization in Deep Networks pdf

Lorenzo Rosasco, Tomaso A. Poggio, Brando Miranda, 2019, ArXiv.

Foreword

James Renegar, Felipe Cucker, Tomaso A. Poggio, 2005, Found. Comput. Math..

Spectro-temporal analysis of speech using 2-d Gabor filters

Tony Ezzat, Tomaso A. Poggio, Jake V. Bouvrie, 2007, INTERSPEECH.

Attention as a Bayesian inference process

Thomas Serre, Tomaso A. Poggio, Sharat Chikkerur, 2011, Electronic Imaging.

Image Representations and Feature Selection for Multimedia Database Search

Tomaso A. Poggio, Massimiliano Pontil, Theodoros Evgeniou, 2003, IEEE Trans. Knowl. Data Eng..

Robust Object Recognition with Cortex-Like Mechanisms

Thomas Serre, Lior Wolf, Tomaso A. Poggio, 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Discriminative template learning in group-convolutional networks for invariant speech representations

Lorenzo Rosasco, Tomaso A. Poggio, Stephen Voinea, 2015, INTERSPEECH.

Biophysiologically Plausible Implementations of the Maximum Operation

Martin A. Giese, Tomaso A. Poggio, Angela J. Yu, 2002, Neural Computation.

Neural-guided, Bidirectional Program Search for Abstraction and Reasoning pdf

Tomaso A. Poggio, Andrzej Banburski, Akshay Rangamani, 2021, COMPLEX NETWORKS.