Tomaso A. Poggio

258 papers A* 30A 8B 9C 2Misc 3Journal 145Unranked 55

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Same Error, Different Function: The Optimizer as an Implicit Prior in Financial Time Series. CoRR Federico Vittorio Cortesi, Giuseppe Iannone, Giulia Crippa, Tomaso A. Poggio, Pierfrancesco Beneventano
2026	J	`jnl`	Tool Building as a Path to "Superintelligence". CoRR David Koplow, Tomer Galanti, Tomaso A. Poggio
2025	J	`jnl`	A universal compression theory: Lottery ticket hypothesis and superpolynomial scaling laws. CoRR Hong-Yi Wang, Di Luo, Tomaso A. Poggio, Isaac L. Chuang, Liu Ziyin
2025	J	`jnl`	Emergence of Hebbian Dynamics in Regularized Non-Local Learners. CoRR David Koplow, Tomaso A. Poggio, Liu Ziyin
2025	A*	`conf`	Formation of Representations in Neural Networks. ICLR Liu Ziyin, Isaac L. Chuang, Tomer Galanti, Tomaso A. Poggio
2025	J	`jnl`	Heterosynaptic Circuits Are Universal Gradient Machines. CoRR Liu Ziyin, Isaac L. Chuang, Tomaso A. Poggio
2025	J	`jnl`	Hierarchical Reasoning Models: Perspectives and Misconceptions. CoRR Renee Ge, Qianli Liao, Tomaso A. Poggio
2025	J	`jnl`	LLM-ERM: Sample-Efficient Program Learning via LLM-Guided Search. CoRR Shivam Singhal, Eran Malach, Tomaso A. Poggio, Tomer Galanti
2025	J	`jnl`	Learning Multi-Index Models with Hyper-Kernel Ridge Regression. CoRR Shuo Huang, Hippolyte Labarrière, Ernesto De Vito, Tomaso A. Poggio, Lorenzo Rosasco
2025	J	`jnl`	On Biologically Plausible Learning in Continuous Time. CoRR Marc Gong Bacvanski, Liu Ziyin, Tomaso A. Poggio
2025	B	`conf`	On Generalization Bounds for Neural Networks with Low Rank Layers. ALT Andrea Pinto, Akshay Rangamani, Tomaso A. Poggio
2025	J	`jnl`	On efficiently computable functions, deep networks and sparse compositionality. CoRR Tomaso A. Poggio
2025	J	`jnl`	Parameter Symmetry Breaking and Restoration Determines the Hierarchical Learning in AI Systems. CoRR Liu Ziyin, Yizhou Xu, Tomaso A. Poggio, Isaac L. Chuang
2025	—	`conf`	Position: A Theory of Deep Learning Must Include Compositional Sparsity. ICML (Position Papers) David A. Danhofer, Davide D'Ascenzo, Rafael Dubach, Tomaso A. Poggio
2025	J	`jnl`	Position: A Theory of Deep Learning Must Include Compositional Sparsity. CoRR David A. Danhofer, Davide D'Ascenzo, Rafael Dubach, Tomaso A. Poggio
2025	—	`conf`	SGD with Weight Decay Secretly Minimizes the Ranks of Your Neural Networks. CPAL Tomer Galanti, Zachary S. Siegel, Aparna Gupte, Tomaso A. Poggio
2025	J	`jnl`	Topological Invariance and Breakdown in Learning. CoRR Yongyi Yang, Tomaso A. Poggio, Isaac L. Chuang, Liu Ziyin
2025	J	`jnl`	Unraveling Syntax: How Language Models Learn Context-Free Grammars. CoRR Laura Ying Schulz, Daniel Mitropolsky, Tomaso A. Poggio
2025	J	`jnl`	What if Eye...? Computationally Recreating Vision Evolution. CoRR Kushagra Tiwary, Aaron Young, Zaid Tasneem, Tzofi Klinghoffer, Akshat Dave, Tomaso A. Poggio, Dan Nilsson, Brian Cheung, Ramesh Raskar
2024	J	`jnl`	Formation of Representations in Neural Networks. CoRR Liu Ziyin, Isaac L. Chuang, Tomer Galanti, Tomaso A. Poggio
2024	J	`jnl`	How Neural Networks Learn the Support is an Implicit Regularization Effect of SGD. CoRR Pierfrancesco Beneventano, Andrea Pinto, Tomaso A. Poggio
2024	J	`jnl`	On Generalization Bounds for Neural Networks with Low Rank Layers. CoRR Andrea Pinto, Akshay Rangamani, Tomaso A. Poggio
2024	A*	`conf`	On the Power of Decision Trees in Auto-Regressive Language Modeling. NeurIPS Yulu Gan, Tomer Galanti, Tomaso A. Poggio, Eran Malach
2024	J	`jnl`	On the Power of Decision Trees in Auto-Regressive Language Modeling. CoRR Yulu Gan, Tomer Galanti, Tomaso A. Poggio, Eran Malach
2024	J	`jnl`	Self-Assembly of a Biologically Plausible Learning Circuit. CoRR Qianli Liao, Liu Ziyin, Yulu Gan, Brian Cheung, Mark Harnett, Tomaso A. Poggio
2024	J	`jnl`	Training the Untrainable: Introducing Inductive Bias via Representational Alignment. CoRR Vighnesh Subramaniam, David Mayo, Colin Conwell, Tomaso A. Poggio, Boris Katz, Brian Cheung, Andrei Barbu
2023	A*	`conf`	Feature learning in deep classifiers through Intermediate Neural Collapse. ICML Akshay Rangamani, Marius Lindegaard, Tomer Galanti, Tomaso A. Poggio
2023	J	`jnl`	How to guess a gradient. CoRR Utkarsh Singhal, Brian Cheung, Kartik Chandra, Jonathan Ragan-Kelley, Joshua B. Tenenbaum, Tomaso A. Poggio, Stella X. Yu
2023	J	`jnl`	Norm-based Generalization Bounds for Compositionally Sparse Neural Networks. CoRR Tomer Galanti, Mengjia Xu, Liane Galanti, Tomaso A. Poggio
2023	A*	`conf`	Norm-based Generalization Bounds for Sparse Neural Networks. NeurIPS Tomer Galanti, Mengjia Xu, Liane Galanti, Tomaso A. Poggio
2023	A*	`conf`	System Identification of Neural Systems: If We Got It Right, Would We Know? ICML Yena Han, Tomaso A. Poggio, Brian Cheung
2023	J	`jnl`	System identification of neural systems: If we got it right, would we know? CoRR Yena Han, Tomaso A. Poggio, Brian Cheung
2022	—	`conf`	Achieving Adversarial Robustness in Deep Learning-Based Overhead Imaging. AIPR Dagen Braun, Matthew D. Reisman, Larry Dewell, Andrzej Banburski-Fahey, Arturo Deza, Tomaso A. Poggio
2022	J	`jnl`	Iterative regularization in classification via hinge loss diagonal descent. CoRR Vassilis Apidopoulos, Tomaso A. Poggio, Lorenzo Rosasco, Silvia Villa
2022	J	`jnl`	Representation Learning in Sensory Cortex: A Theory. IEEE Access Fabio Anselmi, Tomaso A. Poggio
2022	J	`jnl`	SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks. CoRR Tomer Galanti, Tomaso A. Poggio
2021	J	`jnl`	Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows. IEEE Signal Process. Mag. Amir Adler, Mauricio Araya-Polo, Tomaso A. Poggio
2021	J	`jnl`	Distribution of Classification Margins: Are All Data Equal? CoRR Andrzej Banburski, Fernanda De La Torre, Nishka Pant, Ishana Shastri, Tomaso A. Poggio
2021	J	`jnl`	Explicit regularization and implicit bias in deep network classifiers trained with the square loss. CoRR Tomaso A. Poggio, Qianli Liao
2021	—	`conf`	Neural-Guided, Bidirectional Program Search for Abstraction and Reasoning. COMPLEX NETWORKS Simon Alford, Anshula Gandhi, Akshay Rangamani, Andrzej Banburski, Tony Wang, Sylee Dandekar, John Chin, Tomaso A. Poggio, Peter Chin
2021	J	`jnl`	Neural-guided, Bidirectional Program Search for Abstraction and Reasoning. CoRR Simon Alford, Anshula Gandhi, Akshay Rangamani, Andrzej Banburski, Tony Wang, Sylee Dandekar, John Chin, Tomaso A. Poggio, Peter Chin
2021	J	`jnl`	The Effects of Image Distribution and Task on Adversarial Robustness. CoRR Owen Kunhardt, Arturo Deza, Tomaso A. Poggio
2020	J	`jnl`	An analysis of training and generalization errors in shallow and deep networks. Neural Networks Hrushikesh N. Mhaskar, Tomaso A. Poggio
2020	A	`conf`	Approximate Inference with Wasserstein Gradient Flows. AISTATS Charlie Frogner, Tomaso A. Poggio
2020	A*	`conf`	Biologically Inspired Mechanisms for Adversarial Robustness. NeurIPS Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso A. Poggio
2020	J	`jnl`	Biologically Inspired Mechanisms for Adversarial Robustness. CoRR Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso A. Poggio
2020	J	`jnl`	CUDA-Optimized real-time rendering of a Foveated Visual System. CoRR Elian Malkin, Arturo Deza, Tomaso A. Poggio
2020	B	`conf`	Cross-Domain Adversarial Reprogramming of a Recurrent Neural Network. CogSci Alexandra Maria Proca, Andrzej Banburski, Tomaso A. Poggio
2020	J	`jnl`	For interpolating kernel machines, the minimum norm ERM solution is the most stable. CoRR Akshay Rangamani, Lorenzo Rosasco, Tomaso A. Poggio
2020	J	`jnl`	Hierarchically Local Tasks and Deep Convolutional Networks. CoRR Arturo Deza, Qianli Liao, Andrzej Banburski, Tomaso A. Poggio
2019	—	`conf`	Biologically-Plausible Learning Algorithms Can Scale to Large Datasets. ICLR (Poster) Will Xiao, Honglin Chen, Qianli Liao, Tomaso A. Poggio
2019	J	`jnl`	Double descent in the condition number. CoRR Tomaso A. Poggio, Gil Kur, Andrzej Banburski
2019	A*	`conf`	Fast and Flexible Inference of Joint Distributions from their Marginals. ICML Charlie Frogner, Tomaso A. Poggio
2019	A	`conf`	Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. AISTATS Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
2019	J	`jnl`	Function approximation by deep networks. CoRR Hrushikesh N. Mhaskar, Tomaso A. Poggio
2019	J	`jnl`	Symmetry-adapted representation learning. Pattern Recognit. Fabio Anselmi, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio
2019	J	`jnl`	Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization. CoRR Tomaso A. Poggio, Andrzej Banburski, Qianli Liao
2019	J	`jnl`	Theory III: Dynamics and Generalization in Deep Networks. CoRR Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Bob Liang, Jack Hidary, Tomaso A. Poggio
2018	J	`jnl`	A Surprising Linear Relationship Predicts Test Performance in Deep Networks. CoRR Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, Tomaso A. Poggio
2018	J	`jnl`	An analysis of training and generalization errors in shallow and deep networks. CoRR Hrushikesh N. Mhaskar, Tomaso A. Poggio
2018	J	`jnl`	Approximate inference with Wasserstein gradient flows. CoRR Charlie Frogner, Tomaso A. Poggio
2018	J	`jnl`	Biologically-plausible learning algorithms can scale to large datasets. CoRR Will Xiao, Honglin Chen, Qianli Liao, Tomaso A. Poggio
2018	J	`jnl`	Theory IIIb: Generalization in Deep Networks. CoRR Tomaso A. Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary
2018	J	`jnl`	Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar
2018	J	`jnl`	Theory of Deep Learning IIb: Optimization Properties of SGD. CoRR Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso A. Poggio
2017	B	`conf`	Compression of Deep Neural Networks for Image Instance Retrieval. DCC Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso A. Poggio
2017	J	`jnl`	Compression of Deep Neural Networks for Image Instance Retrieval. CoRR Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso A. Poggio
2017	—	`conf`	Do Deep Neural Networks Suffer from Crowding? NIPS Anna Volokitin, Gemma Roig, Tomaso A. Poggio
2017	J	`jnl`	Do Deep Neural Networks Suffer from Crowding? CoRR Anna Volokitin, Gemma Roig, Tomaso A. Poggio
2017	—	`conf`	Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposia Francis X. Chen, Gemma Roig, Leyla Isik, Xavier Boix, Tomaso A. Poggio
2017	J	`jnl`	Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. CoRR Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
2017	—	`conf`	Group Invariant Deep Representations for Image Instance Retrieval. AAAI Spring Symposia Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso A. Poggio
2017	J	`jnl`	Invariant recognition drives neural representations of action sequences. PLoS Comput. Biol. Andrea Tacchetti, Leyla Isik, Tomaso A. Poggio
2017	—	`conf`	Is the Human Visual System Invariant to Translation and Scale? AAAI Spring Symposia Yena Han, Gemma Roig, Gadi Geiger, Tomaso A. Poggio
2017	B	`conf`	Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. ICMR Olivier Morère, Jie Lin, Antoine Veillard, Ling-Yu Duan, Vijay Chandrasekhar, Tomaso A. Poggio
2017	J	`jnl`	Pruning Convolutional Neural Networks for Image Instance Retrieval. CoRR Gaurav Manek, Jie Lin, Vijay Chandrasekhar, Lingyu Duan, Sateesh Giduthuri, Xiaoli Li, Tomaso A. Poggio
2017	—	`conf`	Representation Learning from Orbit Sets for One-Shot Classification. AAAI Spring Symposia Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos, Tomaso A. Poggio
2017	J	`jnl`	Theory II: Landscape of the Empirical Risk in Deep Learning. CoRR Tomaso A. Poggio, Qianli Liao
2017	A*	`conf`	When and Why Are Deep Networks Better Than Shallow Ones? AAAI Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio
2017	J	`jnl`	Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. Int. J. Autom. Comput. Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao
2016	J	`jnl`	Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. CoRR Qianli Liao, Tomaso A. Poggio
2016	J	`jnl`	Deep vs. shallow networks : An approximation theory perspective. CoRR Hrushikesh N. Mhaskar, Tomaso A. Poggio
2016	J	`jnl`	Group Invariant Deep Representations for Image Instance Retrieval. CoRR Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso A. Poggio
2016	A*	`conf`	Holographic Embeddings of Knowledge Graphs. AAAI Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio
2016	A*	`conf`	How Important Is Weight Symmetry in Backpropagation? AAAI Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio
2016	J	`jnl`	Learning Real and Boolean Functions: When Is Deep Better Than Shallow. CoRR Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio
2016	J	`jnl`	Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. CoRR Qianli Liao, Kenji Kawaguchi, Tomaso A. Poggio
2016	J	`jnl`	Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Mag. Tomaso A. Poggio, Ethan Meyers
2016	J	`jnl`	Unsupervised learning of invariant representations. Theor. Comput. Sci. Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio
2016	J	`jnl`	View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. CoRR Joel Z. Leibo, Qianli Liao, Winrich Freiwald, Fabio Anselmi, Tomaso A. Poggio
2016	J	`jnl`	Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review. CoRR Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao
2015	A*	`conf`	Convex Learning of Multiple Tasks and their Structure. ICML Carlo Ciliberto, Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco
2015	J	`jnl`	Convex Learning of Multiple Tasks and their Structure. CoRR Carlo Ciliberto, Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco
2015	J	`jnl`	Deep Convolutional Networks are Hierarchical Kernel Machines. CoRR Fabio Anselmi, Lorenzo Rosasco, Cheston Tan, Tomaso A. Poggio
2015	A	`conf`	Discriminative template learning in group-convolutional networks for invariant speech representations. INTERSPEECH Chiyuan Zhang, Stephen Voinea, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio
2015	J	`jnl`	Foveation-based Mechanisms Alleviate Adversarial Examples. CoRR Yan Luo, Xavier Boix, Gemma Roig, Tomaso A. Poggio, Qi Zhao
2015	J	`jnl`	Holographic Embeddings of Knowledge Graphs. CoRR Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio
2015	J	`jnl`	How Important is Weight Symmetry in Backpropagation? CoRR Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio
2015	—	`conf`	Learning with Group Invariant Features: A Kernel Perspective. NIPS Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio
2015	J	`jnl`	Learning with Group Invariant Features: A Kernel Perspective. CoRR Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio
2015	—	`conf`	Learning with a Wasserstein Loss. NIPS Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, Tomaso A. Poggio
2015	J	`jnl`	Learning with a Wasserstein Loss. CoRR Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, Tomaso A. Poggio
2015	J	`jnl`	On Invariance and Selectivity in Representation Learning. CoRR Fabio Anselmi, Lorenzo Rosasco, Tomaso A. Poggio
2015	J	`jnl`	The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLoS Comput. Biol. Joel Z. Leibo, Qianli Liao, Fabio Anselmi, Tomaso A. Poggio
2014	J	`jnl`	A Deep Representation for Invariance And Music Classification. CoRR Chiyuan Zhang, Georgios Evangelopoulos, Stephen Voinea, Lorenzo Rosasco, Tomaso A. Poggio
2014	Misc	`conf`	A deep representation for invariance and music classification. ICASSP Chiyuan Zhang, Georgios Evangelopoulos, Stephen Voinea, Lorenzo Rosasco, Tomaso A. Poggio
2014	J	`jnl`	Computational role of eccentricity dependent cortical magnification. CoRR Tomaso A. Poggio, Jim Mutch, Leyla Isik
2014	J	`jnl`	Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5). NII Shonan Meet. Rep. Pierre Baldi, Kenji Fukumizu, Tomaso A. Poggio
2014	J	`jnl`	Learning An Invariant Speech Representation. CoRR Georgios Evangelopoulos, Stephen Voinea, Chiyuan Zhang, Lorenzo Rosasco, Tomaso A. Poggio
2014	—	`ch.`	Machine Recognition of Objects. Computer Vision, A Reference Guide Tomaso A. Poggio, Shimon Ullman
2014	J	`jnl`	Neural tuning size is a key factor underlying holistic face processing. CoRR Cheston Tan, Tomaso A. Poggio
2014	A	`conf`	Phone classification by a hierarchy of invariant representation layers. INTERSPEECH Chiyuan Zhang, Stephen Voinea, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio
2014	—	`conf`	Subtasks of Unconstrained Face Recognition. VISAPP (2) Joel Z. Leibo, Qianli Liao, Tomaso A. Poggio
2014	J	`jnl`	Unsupervised learning of clutter-resistant visual representations from natural videos. CoRR Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio
2014	—	`ch.`	Visual Cortex Models for Object Recognition. Computer Vision, A Reference Guide Tomaso A. Poggio, Shimon Ullman
2014	A	`conf`	Word-level invariant representations from acoustic waveforms. INTERSPEECH Stephen Voinea, Chiyuan Zhang, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio
2013	J	`jnl`	Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? CoRR Qianli Liao, Joel Z. Leibo, Youssef Mroueh, Tomaso A. Poggio
2013	—	`conf`	Learning invariant representations and applications to face verification. NIPS Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio
2013	J	`jnl`	Models of visual cortex. Scholarpedia Tomaso A. Poggio, Thomas Serre
2013	—	`conf`	Neural representation of action sequences: how far can a simple snippet-matching model take us? NIPS Cheston Tan, Jedediah M. Singer, Thomas Serre, David L. Sheinberg, Tomaso A. Poggio
2013	J	`jnl`	On Learnability, Complexity and Stability CoRR Silvia Villa, Lorenzo Rosasco, Tomaso A. Poggio
2013	—	`conf`	On Learnability, Complexity and Stability. Empirical Inference Silvia Villa, Lorenzo Rosasco, Tomaso A. Poggio
2013	C	`conf`	The Computational Magic of Pattern Recognition in Cortex: A Theory of Selectivity and Invariance. ICPRAM Tomaso A. Poggio
2013	—	`ch.`	Throwing Down the Visual Intelligence Gauntlet. Machine Learning for Computer Vision Cheston Tan, Joel Z. Leibo, Tomaso A. Poggio
2013	J	`jnl`	Unsupervised Learning of Invariant Representations in Hierarchical Architectures. CoRR Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio
2012	J	`jnl`	Learning Manifolds with K-Means and K-Flats CoRR Guillermo D. Cañas, Tomaso A. Poggio, Lorenzo Rosasco
2012	—	`conf`	Learning Manifolds with K-Means and K-Flats. NIPS Guillermo D. Cañas, Tomaso A. Poggio, Lorenzo Rosasco
2012	J	`jnl`	Learning and disrupting invariance in visual recognition with a temporal association rule. Frontiers Comput. Neurosci. Leyla Isik, Joel Z. Leibo, Tomaso A. Poggio
2012	J	`jnl`	Multiclass Learning with Simplex Coding CoRR Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco, Jean-Jacques E. Slotine
2012	—	`conf`	Multiclass Learning with Simplex Coding. NIPS Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco, Jean-Jacques E. Slotine
2011	—	`conf`	Attention as a Bayesian inference process. Human Vision and Electronic Imaging Sharat Chikkerur, Thomas Serre, Cheston Tan, Tomaso A. Poggio
2011	A*	`conf`	HMDB: A large video database for human motion recognition. ICCV Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso A. Poggio, Thomas Serre
2011	J	`jnl`	Online Learning, Stability, and Stochastic Gradient Descent CoRR Tomaso A. Poggio, Stephen Voinea, Lorenzo Rosasco
2011	—	`conf`	Why The Brain Separates Face Recognition From Object Recognition. NIPS Joel Z. Leibo, Jim Mutch, Tomaso A. Poggio
2010	J	`jnl`	A neuromorphic approach to computer vision. Commun. ACM Thomas Serre, Tomaso A. Poggio
2010	—	`ed.`	Brain Informatics, International Conference, BI 2010, Toronto, ON, Canada, August 28-30, 2010. Proceedings Brain Informatics Yiyu Yao, Ron Sun, Tomaso A. Poggio, Jiming Liu, Ning Zhong, Jimmy X. Huang
2010	—	`conf`	Hierarchical Learning Machines and Neuroscience of Visual Cortex. ECML/PKDD (1) Tomaso A. Poggio
2010	J	`jnl`	Mathematics of the Neural Response. Found. Comput. Math. Steve Smale, Lorenzo Rosasco, Jake V. Bouvrie, Andrea Caponnetto, Tomaso A. Poggio
2010	—	`conf`	Trainable, vision-based automated home cage behavioral phenotyping. MB Hueihan Jhuang, Estíbaliz Garrote, Nicholas Edelman, Tomaso A. Poggio, Andrew Steele, Thomas Serre
2009	—	`conf`	On Invariance in Hierarchical Models. NIPS Jake V. Bouvrie, Lorenzo Rosasco, Tomaso A. Poggio
2008	J	`jnl`	A Canonical Neural Circuit for Cortical Nonlinear Operations. Neural Comput. Minjoon Kouh, Tomaso A. Poggio
2008	—	`conf`	Discriminative word-spotting using ordered spectro-temporal patch features. SAPA@INTERSPEECH Tony Ezzat, Tomaso A. Poggio
2008	Misc	`conf`	Localized spectro-temporal cepstral analysis of speech. ICASSP Jake V. Bouvrie, Tony Ezzat, Tomaso A. Poggio
2007	A*	`conf`	A Biologically Inspired System for Action Recognition. ICCV Hueihan Jhuang, Thomas Serre, Lior Wolf, Tomaso A. Poggio
2007	J	`jnl`	A Component-based Framework for Face Detection and Identification. Int. J. Comput. Vis. Bernd Heisele, Thomas Serre, Tomaso A. Poggio
2007	—	`conf`	AM-FM Demodulation of Spectrograms using Localized 2D Max-Gabor Analysis. ICASSP (4) Tony Ezzat, Jake V. Bouvrie, Tomaso A. Poggio
2007	J	`jnl`	Robust Object Recognition with Cortex-Like Mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. Thomas Serre, Lior Wolf, Stanley M. Bileschi, Maximilian Riesenhuber, Tomaso A. Poggio
2007	A	`conf`	Spectro-temporal analysis of speech using 2-d Gabor filters. INTERSPEECH Tony Ezzat, Jake V. Bouvrie, Tomaso A. Poggio
2006	—	`conf`	A Multiview Face Identification Model With No Geometric Constraints. FGR Jerry Jun Yokono, Tomaso A. Poggio
2006	—	`conf`	Biophysical Models of Neural Computation: Max and Tuning Circuits. WImBI Ulf Knoblich, Jake V. Bouvrie, Tomaso A. Poggio
2006	J	`jnl`	Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization. Adv. Comput. Math. Sayan Mukherjee, Partha Niyogi, Tomaso A. Poggio, Ryan M. Rifkin
2006	A	`conf`	Max-Gabor analysis and synthesis of spectrograms. INTERSPEECH Tony Ezzat, Jake V. Bouvrie, Tomaso A. Poggio
2006	—	`conf`	Neuroscience: New Insights for AI? ICDM / Web Intelligence / IAT Tomaso A. Poggio
2006	—	`conf`	Neuroscience: New Insights for AI? WImBI Tomaso A. Poggio
2005	J	`jnl`	Foreword. Found. Comput. Math. Lenore Blum, Felipe Cucker, Tomaso A. Poggio, James Renegar, Michael Shub
2005	—	`conf`	Learning Features of Intermediate Complexity for the Recognition of Biological Motion. ICANN (1) Rodrigo Sigala, Thomas Serre, Tomaso A. Poggio, Martin A. Giese
2005	A	`conf`	Morphing spectral envelopes using audio flow. INTERSPEECH Tony Ezzat, Ethan Meyers, James R. Glass, Tomaso A. Poggio
2005	—	`conf`	Object Recognition with Features Inspired by Visual Cortex. CVPR (2) Thomas Serre, Lior Wolf, Tomaso A. Poggio
2004	—	`conf`	Oriented Filters for Object Recognition: an Empirical Study. FGR Jerry Jun Yokono, Tomaso A. Poggio
2004	—	`conf`	Trainable Videorealistic Speech Animation. FGR Tony Ezzat, Gadi Geiger, Tomaso A. Poggio
2003	J	`jnl`	An Analytical Method for Multiclass Molecular Cancer Classification. SIAM Rev. Ryan M. Rifkin, Sayan Mukherjee, Pablo Tamayo, Sridhar Ramaswamy, Chen-Hsiang Yeang, Michael Angelo, Michael Reich, Tomaso A. Poggio, Eric S. Lander, Todd R. Golub, Jill P. Mesirov
2003	J	`jnl`	Face recognition: component-based versus global approaches. Comput. Vis. Image Underst. Bernd Heisele, Purdy Ho, Jane Wu, Tomaso A. Poggio
2003	J	`jnl`	Full-body person recognition system. Pattern Recognit. Chikahito Nakajima, Massimiliano Pontil, Bernd Heisele, Tomaso A. Poggio
2003	J	`jnl`	Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognit. Bernd Heisele, Thomas Serre, Sam Prentice, Tomaso A. Poggio
2003	J	`jnl`	Image Representations and Feature Selection for Multimedia Database Search. IEEE Trans. Knowl. Data Eng. Theodoros Evgeniou, Massimiliano Pontil, Constantine Papageorgiou, Tomaso A. Poggio
2003	—	`conf`	Learning and Perceptual Interfaces. CVPR Workshops Tomaso A. Poggio
2003	J	`jnl`	Reanimating Faces in Images and Video. Comput. Graph. Forum Volker Blanz, Curzio Basso, Tomaso A. Poggio, Thomas Vetter
2002	—	`conf`	Attentional Selection for Object Recognition - A Gentle Way. Biologically Motivated Computer Vision Dirk B. Walther, Laurent Itti, Maximilian Riesenhuber, Tomaso A. Poggio, Christof Koch
2002	—	`ed.`	Biologically Motivated Computer Vision Second International Workshop, BMCV 2002, Tübingen, Germany, November 22-24, 2002, Proceedings Biologically Motivated Computer Vision Heinrich H. Bülthoff, Seong-Whan Lee, Tomaso A. Poggio, Christian Wallraven
2002	J	`jnl`	Biophysiologically Plausible Implementations of the Maximum Operation. Neural Comput. Angela J. Yu, Martin A. Giese, Tomaso A. Poggio
2002	J	`jnl`	Learning and vision machines. Proc. IEEE Bernd Heisele, Alessandro Verri, Tomaso A. Poggio
2002	—	`conf`	On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision. Biologically Motivated Computer Vision Thomas Serre, Maximilian Riesenhuber, Jennifer Louie, Tomaso A. Poggio
2002	—	`conf`	Recognizing Expressions by Direct Estimation of the Parameters of a Pixel Morphable Model. Biologically Motivated Computer Vision Vinay P. Kumar, Tomaso A. Poggio
2002	J	`jnl`	Trainable videorealistic speech animation. ACM Trans. Graph. Tony Ezzat, Gadi Geiger, Tomaso A. Poggio
2002	—	`conf`	Visual Categorization: How the Monkey Brain Does It. Biologically Motivated Computer Vision Ulf Knoblich, Maximilian Riesenhuber, David J. Freedman, Earl K. Miller, Tomaso A. Poggio
2001	—	`conf`	Categorization by Learning and Combining Object Parts. NIPS Bernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso A. Poggio
2001	—	`conf`	Component-based Face Detection. CVPR (1) Bernd Heisele, Thomas Serre, Massimiliano Pontil, Tomaso A. Poggio
2001	J	`jnl`	Example-Based Object Detection in Images by Components. IEEE Trans. Pattern Anal. Mach. Intell. Anuj Mohan, Constantine Papageorgiou, Tomaso A. Poggio
2001	A*	`conf`	Face Recognition with Support Vector Machines: Global versus Component-based Approach. ICCV Bernd Heisele, Purdy Ho, Tomaso A. Poggio
2001	—	`conf`	Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images. CVPR (2) Bernd Heisele, Thomas Serre, Sayan Mukherjee, Tomaso A. Poggio
2000	J	`jnl`	A Trainable System for Object Detection. Int. J. Comput. Vis. Constantine Papageorgiou, Tomaso A. Poggio
2000	—	`ed.`	Biologically Motivated Computer Vision, First IEEE International Workshop, BMVC 2000, Seoul, Korea, May 15-17, 2000, Proceedings Biologically Motivated Computer Vision Seong-Whan Lee, Heinrich H. Bülthoff, Tomaso A. Poggio
2000	A*	`conf`	Bounds on the Generalization Performance of Kernel Machine Ensembles. ICML Theodoros Evgeniou, Luis Pérez-Breva, Massimiliano Pontil, Tomaso A. Poggio
2000	—	`conf`	CBF: A New Framework for Object Categorization in Cortex. Biologically Motivated Computer Vision Maximilian Riesenhuber, Tomaso A. Poggio
2000	—	`conf`	Feature Selection for SVMs. NIPS Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso A. Poggio, Vladimir Vapnik
2000	—	`conf`	Incremental and Decremental Support Vector Machine Learning. NIPS Gert Cauwenberghs, Tomaso A. Poggio
2000	J	`jnl`	Introduction: Learning and Vision at CBCL. Int. J. Comput. Vis. Tomaso A. Poggio, Alessandro Verri
2000	B	`conf`	Learning-Based Approach to Real Time Tracking and Analysis of Faces. FG Vinay P. Kumar, Tomaso A. Poggio
2000	J	`jnl`	Morphable Models for the Analysis and Synthesis of Complex Motion Patterns. Int. J. Comput. Vis. Martin A. Giese, Tomaso A. Poggio
2000	B	`conf`	Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation. ICPR Chikahito Nakajima, Norihiko Itoh, Massimiliano Pontil, Tomaso A. Poggio
2000	—	`conf`	People Recognition and Pose Estimation in Image Sequences. IJCNN (4) Chikahito Nakajima, Massimiliano Pontil, Tomaso A. Poggio
2000	J	`jnl`	Regularization Networks and Support Vector Machines. Adv. Comput. Math. Theodoros Evgeniou, Massimiliano Pontil, Tomaso A. Poggio
2000	J	`jnl`	Statistical Learning Theory: A Primer. Int. J. Comput. Vis. Theodoros Evgeniou, Massimiliano Pontil, Tomaso A. Poggio
2000	J	`jnl`	Visual Speech Synthesis by Morphing Visemes. Int. J. Comput. Vis. Tony Ezzat, Tomaso A. Poggio
1999	A*	`conf`	A Pattern Classification Approach to Dynamical Object Detection. ICCV Constantine Papageorgiou, Tomaso A. Poggio
1999	J	`jnl`	GEM: A Global Electronic Market System. Inf. Syst. Benny Rachlevsky-Reich, Israel Ben-Shaul, Nicholas Tung Chan, Andrew W. Lo, Tomaso A. Poggio
1999	J	`jnl`	Machine Learning, Machine Vision, and the Brain. AI Mag. Tomaso A. Poggio, Christian R. Shelton
1999	Misc	`conf`	Sparse correlation kernel reconstruction. ICASSP Constantine Papageorgiou, Federico Girosi, Tomaso A. Poggio
1999	—	`conf`	Trainable Pedestrian Detection. ICIP (4) Constantine Papageorgiou, Tomaso A. Poggio
1998	A*	`conf`	A General Framework for Object Detection. ICCV Constantine Papageorgiou, Michael Oren, Tomaso A. Poggio
1998	J	`jnl`	A Sparse Representation For Function Approximation. Neural Comput. Tomaso A. Poggio, Federico Girosi
1998	J	`jnl`	Example-Based Learning for View-Based Human Face Detection. IEEE Trans. Pattern Anal. Mach. Intell. Kah Kay Sung, Tomaso A. Poggio
1998	J	`jnl`	Guest Editorial Applications Of Artificial Neural Networks To Image Processing. IEEE Trans. Image Process. Rama Chellappa, Kunihiko Fukushima, Aggelos K. Katsaggelos, Sun-Yuan Kung, Yann LeCun, Nasser M. Nasrabadi, Tomaso A. Poggio
1998	A*	`conf`	Hierarchical Morphable Models. CVPR Michael J. Jones, Tomaso A. Poggio
1998	J	`jnl`	Incorporating prior information in machine learning by creating virtual examples. Proc. IEEE Partha Niyogi, Federico Girosi, Tomaso A. Poggio
1998	—	`conf`	MikeTalk: A Talking Facial Display Based on Morphing Visemes. CA Tony Ezzat, Tomaso A. Poggio
1998	A*	`conf`	Multidimensional Morphable Models. ICCV Michael J. Jones, Tomaso A. Poggio
1998	J	`jnl`	Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes. Int. J. Comput. Vis. Michael J. Jones, Tomaso A. Poggio
1997	A*	`conf`	A bootstrapping algorithm for learning linear models of object classes. CVPR Thomas Vetter, Michael J. Jones, Tomaso A. Poggio
1997	J	`jnl`	Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. Bernhard Schölkopf, Kah Kay Sung, Christopher J. C. Burges, Federico Girosi, Partha Niyogi, Tomaso A. Poggio, Vladimir Vapnik
1997	J	`jnl`	Guest Editors' Introduction: Neural Networks For Signal Processing. IEEE Trans. Signal Process. Anthony G. Constantinides, Simon Haykin, Yu Hen Hu, Jenq-Neng Hwang, Shigeru Katagiri, Sun-Yuan Kung, Tomaso A. Poggio
1997	—	`conf`	Image Representations for Visual Learning (Invited Paper). AVBPA Tomaso A. Poggio
1997	B	`conf`	Image-based view synthesis by combining trilinear tensors and learning techniques. VRST Shai Avidan, Theodoros Evgeniou, Amnon Shashua, Tomaso A. Poggio
1997	—	`conf`	Just One View: Invariances in Inferotemporal Cell Tuning. NIPS Maximilian Riesenhuber, Tomaso A. Poggio
1997	J	`jnl`	Linear Object Classes and Image Synthesis From a Single Example Image. IEEE Trans. Pattern Anal. Mach. Intell. Thomas Vetter, Tomaso A. Poggio
1997	A*	`conf`	Pedestrian Detection Using Wavelet Templates. CVPR Michael Oren, Constantine Papageorgiou, Pawan Sinha, Edgar Osuna, Tomaso A. Poggio
1997	J	`jnl`	Template matching: matched spatial filters and beyond. Pattern Recognit. Roberto Brunelli, Tomaso A. Poggio
1997	—	`conf`	Videorealistic talking faces: a morphing approach. AVSP Tony Ezzat, Tomaso A. Poggio
1996	—	`conf`	3D Object Recognition: A Model of View-Tuned Neurons. NIPS Emanuela Bricolo, Tomaso A. Poggio, Nikos K. Logothetis
1996	B	`conf`	Facial Analysis and Synthesis Using Image-Based Models. FG Tony Ezzat, Tomaso A. Poggio
1996	—	`conf`	Image Synthesis from a Single Example Image. ECCV (1) Thomas Vetter, Tomaso A. Poggio
1995	J	`jnl`	Automatic person recognition by acoustic and geometric features. Mach. Vis. Appl. Roberto Brunelli, Daniele Falavigna, Tomaso A. Poggio, Luigi Stringa
1995	A*	`conf`	Face Recognition from One Example View. ICCV David Beymer, Tomaso A. Poggio
1995	B	`conf`	Finding Human Faces with a Gaussian Mixture Distribution-Based Face Model. ACCV Tomaso A. Poggio, Kah Kay Sung
1995	C	`conf`	Learning Human Face Detection in Cluttered Scenes. CAIP Kah Kay Sung, Tomaso A. Poggio
1995	A*	`conf`	Model-Based Matching of Line Drawings by Linear Combinations of Prototypes. ICCV Michael J. Jones, Tomaso A. Poggio
1995	J	`jnl`	Optical flow from 1-D correlation: Application to a simple time-to-crash detector. Int. J. Comput. Vis. Nicola Ancona, Tomaso A. Poggio
1995	J	`jnl`	Regularization Theory and Neural Networks Architectures. Neural Comput. Federico Girosi, Michael J. Jones, Tomaso A. Poggio
1994	J	`jnl`	Introduction to the Special Section on Learning in Computer Vision. IEEE Trans. Pattern Anal. Mach. Intell. Bir Bhanu, Tomaso A. Poggio
1993	J	`jnl`	Caricatural effects in automated face perception. Biol. Cybern. Roberto Brunelli, Tomaso A. Poggio
1993	J	`jnl`	Face Recognition: Features Versus Templates. IEEE Trans. Pattern Anal. Mach. Intell. Roberto Brunelli, Tomaso A. Poggio
1993	A*	`conf`	Optical flow from 1D correlation: Application to a simple time-to-crash detector. ICCV Nicola Ancona, Tomaso A. Poggio
1993	J	`jnl`	Report on Workshop on High Performance Computing and Communications for Grand Challenge Applications: Computer Vision, Speech and Natural Language Processing, and Artificial Intelligence. IEEE Trans. Knowl. Data Eng. 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, Lynette Hirschman, Richard E. Korf, Stephen E. Levinson, Daniel P. Miranker, N. H. Morgan, Sergei Nirenburg, Tomaso A. Poggio, Edward M. Riseman, Craig Stanfil, Salvatore J. Stolfo, Steven L. Tanimoto, Charles C. Weems
1993	J	`jnl`	Werner Reichardt 1924-1992. Biol. Cybern. Tomaso A. Poggio
1992	J	`jnl`	Analog VLSI systems for image acquisition and fast early vision processing. Int. J. Comput. Vis. 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
1992	J	`jnl`	Bringing the Grandmother back into the Picture: A Memory-Based View of Object Recognition. Int. J. Pattern Recognit. Artif. Intell. Shimon Edelman, Tomaso A. Poggio
1992	A*	`conf`	Face Recognition through Geometrical Features. ECCV Roberto Brunelli, Tomaso A. Poggio
1992	J	`jnl`	Learning of visual modules from examples: A framework for understanding adaptive visual performance. CVGIP Image Underst. Tomaso A. Poggio, Shimon Edelman, Manfred Fahle
1991	A*	`conf`	HyperBF Networks for Real Object Recognition. IJCAI Roberto Brunelli, Tomaso A. Poggio
1991	J	`jnl`	Parallel Processing in the DARPA Strategic Computing Vision Program. IEEE Expert Charles C. Weems, Christopher M. Brown, Jon A. Webb, Tomaso A. Poggio, John R. Kender
1990	—	`conf`	Extensions of a Theory of Networks for Approximation and Learning. NIPS Federico Girosi, Tomaso A. Poggio, Bruno Caprile
1990	J	`jnl`	Networks for approximation and learning. Proc. IEEE Tomaso A. Poggio, Federico Girosi
1989	J	`jnl`	Integration of vision modules and labeling of surface discontinuities. IEEE Trans. Syst. Man Cybern. Edward B. Gamble, Davi Geiger, Tomaso A. Poggio, Daphna Weinshall
1989	J	`jnl`	Motion Field and Optical Flow: Qualitative Properties. IEEE Trans. Pattern Anal. Mach. Intell. Alessandro Verri, Tomaso A. Poggio
1989	J	`jnl`	Representation Properties of Networks: Kolmogorov's Theorem Is Irrelevant. Neural Comput. Federico Girosi, Tomaso A. Poggio
1988	—	`conf`	A Network for Image Segmentation Using Color. NIPS Anya C. Hurlbert, Tomaso A. Poggio
1988	J	`jnl`	A regularized solution to edge detection. J. Complex. Tomaso A. Poggio, Harry Voorhees, Alan L. Yuille
1988	J	`jnl`	Ill-posed problems in early vision. Proc. IEEE Mario Bertero, Tomaso A. Poggio, Vincent Torre
1988	J	`jnl`	Learning, regularization and splines. Neural Networks Tomaso A. Poggio
1988	A*	`conf`	Parallel Optical Flow Using Local Voting. ICCV James J. Little, Heinrich H. Bülthoff, Tomaso A. Poggio
1988	J	`jnl`	Representation properties of multilayer feedforward networks. Neural Networks B. Moore, Tomaso A. Poggio
1987	A*	`conf`	An Optimal Scale for Edge Detection. IJCAI Davi Geiger, Tomaso A. Poggio
1987	—	`conf`	Learning a Color Algorithm from Examples. NIPS Tomaso A. Poggio, Anya C. Hurlbert
1986	J	`jnl`	On Edge Detection. IEEE Trans. Pattern Anal. Mach. Intell. Vincent Torre, Tomaso A. Poggio
1986	A*	`conf`	On parallel stereo. ICRA Michael Drumheller, Tomaso A. Poggio
1986	J	`jnl`	Scaling Theorems for Zero Crossings. IEEE Trans. Pattern Anal. Mach. Intell. Alan L. Yuille, Tomaso A. Poggio
1985	J	`jnl`	Early vision: From computational structure to algorithms and parallel hardware. Comput. Vis. Graph. Image Process. Tomaso A. Poggio
1984	A*	`conf`	Fingerprints Theorems. AAAI Alan L. Yuille, Tomaso A. Poggio