Tomaso A. Poggio

ORCID

Publication Activity (10 Years)

Years Active: 1984-2023
Publications (10 Years): 78

Top Venues

Publications

Utkarsh Singhal, Brian Cheung, Kartik Chandra, Jonathan Ragan-Kelley, Joshua B. Tenenbaum, Tomaso A. Poggio, Stella X. Yu
How to guess a gradient. CoRR (2023)
Akshay Rangamani, Marius Lindegaard, Tomer Galanti, Tomaso A. Poggio
Feature learning in deep classifiers through Intermediate Neural Collapse. ICML (2023)
Yena Han, Tomaso A. Poggio, Brian Cheung
System identification of neural systems: If we got it right, would we know? CoRR (2023)
Tomer Galanti, Mengjia Xu, Liane Galanti, Tomaso A. Poggio
Norm-based Generalization Bounds for Compositionally Sparse Neural Networks. CoRR (2023)
Yena Han, Tomaso A. Poggio, Brian Cheung
System Identification of Neural Systems: If We Got It Right, Would We Know? ICML (2023)
Tomer Galanti, Mengjia Xu, Liane Galanti, Tomaso A. Poggio
Norm-based Generalization Bounds for Sparse Neural Networks. NeurIPS (2023)
Dagen Braun, Matthew Reisman, Larry Dewell, Andrzej Banburski-Fahey, Arturo Deza, Tomaso A. Poggio
Achieving Adversarial Robustness in Deep Learning-Based Overhead Imaging. AIPR (2022)
Fabio Anselmi, Tomaso A. Poggio
Representation Learning in Sensory Cortex: A Theory. IEEE Access 10 (2022)
Tomer Galanti, Tomaso A. Poggio
SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks. CoRR (2022)
Vassilis Apidopoulos, Tomaso A. Poggio, Lorenzo Rosasco, Silvia Villa
Iterative regularization in classification via hinge loss diagonal descent. CoRR (2022)
Simon Alford, Anshula Gandhi, Akshay Rangamani, Andrzej Banburski, Tony Wang, Sylee Dandekar, John Chin, Tomaso A. Poggio, Sang Peter Chin
Neural-guided, Bidirectional Program Search for Abstraction and Reasoning. CoRR (2021)
Amir Adler, Mauricio Araya-Polo, Tomaso A. Poggio
Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows. IEEE Signal Process. Mag. 38 (2) (2021)
Tomaso A. Poggio, Qianli Liao
Explicit regularization and implicit bias in deep network classifiers trained with the square loss. CoRR (2021)
Andrzej Banburski, Fernanda De La Torre, Nishka Pant, Ishana Shastri, Tomaso A. Poggio
Distribution of Classification Margins: Are All Data Equal? CoRR (2021)
Owen Kunhardt, Arturo Deza, Tomaso A. Poggio
The Effects of Image Distribution and Task on Adversarial Robustness. CoRR (2021)
Simon Alford, Anshula Gandhi, Akshay Rangamani, Andrzej Banburski, Tony Wang, Sylee Dandekar, John Chin, Tomaso A. Poggio, Peter Chin
Neural-Guided, Bidirectional Program Search for Abstraction and Reasoning. COMPLEX NETWORKS (2021)
Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso A. Poggio
Biologically Inspired Mechanisms for Adversarial Robustness. CoRR (2020)
Akshay Rangamani, Lorenzo Rosasco, Tomaso A. Poggio
For interpolating kernel machines, the minimum norm ERM solution is the most stable. CoRR (2020)
Alexandra Proca, Andrzej Banburski, Tomaso A. Poggio
Cross-Domain Adversarial Reprogramming of a Recurrent Neural Network. CogSci (2020)
Hrushikesh N. Mhaskar, Tomaso A. Poggio
An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121 (2020)
Elian Malkin, Arturo Deza, Tomaso A. Poggio
CUDA-Optimized real-time rendering of a Foveated Visual System. CoRR (2020)
Charlie Frogner, Tomaso A. Poggio
Approximate Inference with Wasserstein Gradient Flows. AISTATS (2020)
Arturo Deza, Qianli Liao, Andrzej Banburski, Tomaso A. Poggio
Hierarchically Local Tasks and Deep Convolutional Networks. CoRR (2020)
Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso A. Poggio
Biologically Inspired Mechanisms for Adversarial Robustness. NeurIPS (2020)
Tomaso A. Poggio, Gil Kur, Andrzej Banburski
Double descent in the condition number. CoRR (2019)
Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Bob Liang, Jack Hidary, Tomaso A. Poggio
Theory III: Dynamics and Generalization in Deep Networks. CoRR (2019)
Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. AISTATS (2019)
Tomaso A. Poggio, Andrzej Banburski, Qianli Liao
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization. CoRR (2019)
Hrushikesh N. Mhaskar, Tomaso A. Poggio
Function approximation by deep networks. CoRR (2019)
Charlie Frogner, Tomaso A. Poggio
Fast and Flexible Inference of Joint Distributions from their Marginals. ICML (2019)
Will Xiao, Honglin Chen, Qianli Liao, Tomaso A. Poggio
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets. ICLR (Poster) (2019)
Fabio Anselmi, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio
Symmetry-adapted representation learning. Pattern Recognit. 86 (2019)
Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso A. Poggio
Theory of Deep Learning IIb: Optimization Properties of SGD. CoRR (2018)
Charlie Frogner, Tomaso A. Poggio
Approximate inference with Wasserstein gradient flows. CoRR (2018)
Will Xiao, Honglin Chen, Qianli Liao, Tomaso A. Poggio
Biologically-plausible learning algorithms can scale to large datasets. CoRR (2018)
Tomaso A. Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary
Theory IIIb: Generalization in Deep Networks. CoRR (2018)
Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, Tomaso A. Poggio
A Surprising Linear Relationship Predicts Test Performance in Deep Networks. CoRR (2018)
Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar
Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR (2018)
Hrushikesh N. Mhaskar, Tomaso A. Poggio
An analysis of training and generalization errors in shallow and deep networks. CoRR (2018)
Andrea Tacchetti, Leyla Isik, Tomaso A. Poggio
Invariant recognition drives neural representations of action sequences. PLoS Comput. Biol. 13 (12) (2017)
Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos, Tomaso A. Poggio
Representation Learning from Orbit Sets for One-Shot Classification. AAAI Spring Symposia (2017)
Tomaso A. Poggio, Qianli Liao
Theory II: Landscape of the Empirical Risk in Deep Learning. CoRR (2017)
Francis X. Chen, Gemma Roig, Leyla Isik, Xavier Boix, Tomaso A. Poggio
Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposia (2017)
Gaurav Manek, Jie Lin, Vijay Chandrasekhar, Lingyu Duan, Sateesh Giduthuri, Xiaoli Li, Tomaso A. Poggio
Pruning Convolutional Neural Networks for Image Instance Retrieval. CoRR (2017)
Anna Volokitin, Gemma Roig, Tomaso A. Poggio
Do Deep Neural Networks Suffer from Crowding? NIPS (2017)
Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso A. Poggio
Group Invariant Deep Representations for Image Instance Retrieval. AAAI Spring Symposia (2017)
Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio
When and Why Are Deep Networks Better Than Shallow Ones? AAAI (2017)
Olivier Morère, Jie Lin, Antoine Veillard, Ling-Yu Duan, Vijay Chandrasekhar, Tomaso A. Poggio
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. ICMR (2017)
Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso A. Poggio
Compression of Deep Neural Networks for Image Instance Retrieval. DCC (2017)
Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. Int. J. Autom. Comput. 14 (5) (2017)
Anna Volokitin, Gemma Roig, Tomaso A. Poggio
Do Deep Neural Networks Suffer from Crowding? CoRR (2017)
Yena Han, Gemma Roig, Gadi Geiger, Tomaso A. Poggio
Is the Human Visual System Invariant to Translation and Scale? AAAI Spring Symposia (2017)
Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. CoRR (2017)
Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso A. Poggio
Compression of Deep Neural Networks for Image Instance Retrieval. CoRR (2017)
Qianli Liao, Tomaso A. Poggio
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. CoRR (2016)
Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio
Holographic Embeddings of Knowledge Graphs. AAAI (2016)
Hrushikesh N. Mhaskar, Tomaso A. Poggio
Deep vs. shallow networks : An approximation theory perspective. CoRR (2016)
Joel Z. Leibo, Qianli Liao, Winrich Freiwald, Fabio Anselmi, Tomaso A. Poggio
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. CoRR (2016)
Tomaso A. Poggio, Ethan Meyers
Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Mag. 37 (1) (2016)
Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio
Unsupervised learning of invariant representations. Theor. Comput. Sci. 633 (2016)
Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio
Learning Real and Boolean Functions: When Is Deep Better Than Shallow. CoRR (2016)
Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso A. Poggio
Group Invariant Deep Representations for Image Instance Retrieval. CoRR (2016)
Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio
How Important Is Weight Symmetry in Backpropagation? AAAI (2016)
Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao
Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review. CoRR (2016)
Qianli Liao, Kenji Kawaguchi, Tomaso A. Poggio
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. CoRR (2016)
Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio
Learning with Group Invariant Features: A Kernel Perspective. NIPS (2015)
Fabio Anselmi, Lorenzo Rosasco, Tomaso A. Poggio
On Invariance and Selectivity in Representation Learning. CoRR (2015)
Carlo Ciliberto, Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco
Convex Learning of Multiple Tasks and their Structure. CoRR (2015)
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, Tomaso A. Poggio
Learning with a Wasserstein Loss. CoRR (2015)
Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio
Learning with Group Invariant Features: A Kernel Perspective. CoRR (2015)
Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio
How Important is Weight Symmetry in Backpropagation? CoRR (2015)
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya-Polo, Tomaso A. Poggio
Learning with a Wasserstein Loss. NIPS (2015)
Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio
Holographic Embeddings of Knowledge Graphs. CoRR (2015)
Joel Z. Leibo, Qianli Liao, Fabio Anselmi, Tomaso A. Poggio
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLoS Comput. Biol. 11 (10) (2015)
Yan Luo, Xavier Boix, Gemma Roig, Tomaso A. Poggio, Qi Zhao
Foveation-based Mechanisms Alleviate Adversarial Examples. CoRR (2015)
Fabio Anselmi, Lorenzo Rosasco, Cheston Tan, Tomaso A. Poggio
Deep Convolutional Networks are Hierarchical Kernel Machines. CoRR (2015)
Chiyuan Zhang, Stephen Voinea, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio
Discriminative template learning in group-convolutional networks for invariant speech representations. INTERSPEECH (2015)
Carlo Ciliberto, Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco
Convex Learning of Multiple Tasks and their Structure. ICML (2015)
Cheston Tan, Tomaso A. Poggio
Neural tuning size is a key factor underlying holistic face processing. CoRR (2014)
Tomaso A. Poggio, Jim Mutch, Leyla Isik
Computational role of eccentricity dependent cortical magnification. CoRR (2014)
Chiyuan Zhang, Stephen Voinea, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio
Phone classification by a hierarchy of invariant representation layers. INTERSPEECH (2014)
Pierre Baldi, Kenji Fukumizu, Tomaso A. Poggio
Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5). NII Shonan Meet. Rep. 2014 (2014)
Chiyuan Zhang, Georgios Evangelopoulos, Stephen Voinea, Lorenzo Rosasco, Tomaso A. Poggio
A deep representation for invariance and music classification. ICASSP (2014)
Chiyuan Zhang, Georgios Evangelopoulos, Stephen Voinea, Lorenzo Rosasco, Tomaso A. Poggio
A Deep Representation for Invariance And Music Classification. CoRR (2014)
Georgios Evangelopoulos, Stephen Voinea, Chiyuan Zhang, Lorenzo Rosasco, Tomaso A. Poggio
Learning An Invariant Speech Representation. CoRR (2014)
Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio
Unsupervised learning of clutter-resistant visual representations from natural videos. CoRR (2014)
Stephen Voinea, Chiyuan Zhang, Georgios Evangelopoulos, Lorenzo Rosasco, Tomaso A. Poggio
Word-level invariant representations from acoustic waveforms. INTERSPEECH (2014)
Joel Z. Leibo, Qianli Liao, Tomaso A. Poggio
Subtasks of Unconstrained Face Recognition. VISAPP (2) (2014)
Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio
Learning invariant representations and applications to face verification. NIPS (2013)
Tomaso A. Poggio, Thomas Serre
Models of visual cortex. Scholarpedia 8 (4) (2013)
Silvia Villa, Lorenzo Rosasco, Tomaso A. Poggio
On Learnability, Complexity and Stability CoRR (2013)
Silvia Villa, Lorenzo Rosasco, Tomaso A. Poggio
On Learnability, Complexity and Stability. Empirical Inference (2013)
Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso A. Poggio
Unsupervised Learning of Invariant Representations in Hierarchical Architectures. CoRR (2013)
Qianli Liao, Joel Z. Leibo, Youssef Mroueh, Tomaso A. Poggio
Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? CoRR (2013)
Cheston Tan, Jedediah M. Singer, Thomas Serre, David Sheinberg, Tomaso A. Poggio
Neural representation of action sequences: how far can a simple snippet-matching model take us? NIPS (2013)
Tomaso A. Poggio
The Computational Magic of Pattern Recognition in Cortex: A Theory of Selectivity and Invariance. ICPRAM (2013)
Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco, Jean-Jacques E. Slotine
Multiclass Learning with Simplex Coding. NIPS (2012)
Guillermo D. Cañas, Tomaso A. Poggio, Lorenzo Rosasco
Learning Manifolds with K-Means and K-Flats. NIPS (2012)
Guillermo D. Cañas, Tomaso A. Poggio, Lorenzo Rosasco
Learning Manifolds with K-Means and K-Flats CoRR (2012)
Leyla Isik, Joel Z. Leibo, Tomaso A. Poggio
Learning and disrupting invariance in visual recognition with a temporal association rule. Frontiers Comput. Neurosci. 6 (2012)

Tomaso A. Poggio.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Tomaso A. Poggio