T. Poggio

Most-cited papers

Automated fault detection without seismic processing
M. Araya-Polo, T. Dahlke, Charlie Frogner, Chiyuan Zhang +2 more
2017230 citations Open access
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Theoretical issues in deep networks
T. Poggio, Andrzej Banburski, Q. Liao
2019Proceedings of the National Academy of Sciences of the United States of America212 citations Open access
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Theory of Deep Learning III: explaining the non-overfitting puzzle
T. Poggio, Kenji Kawaguchi, Q. Liao, B. Miranda +4 more
2017arXiv.org140 citations
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Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows
Amir Adler, M. Araya-Polo, T. Poggio
2021IEEE Signal Processing Magazine122 citations Open access
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Biologically-plausible learning algorithms can scale to large datasets
Will Xiao, Honglin Chen, Q. Liao, T. Poggio
2018International Conference on Learning Representations91 citations
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Theory II: Landscape of the Empirical Risk in Deep Learning
T. Poggio, Q. Liao
2017arXiv.org81 citations
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Feature learning in deep classifiers through Intermediate Neural Collapse
Akshay Rangamani, Marius Lindegaard, Tomer Galanti, T. Poggio
2023International Conference on Machine Learning77 citations
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Theory of Deep Learning IIb: Optimization Properties of SGD
Chiyuan Zhang, Q. Liao, A. Rakhlin, B. Miranda +2 more
2018arXiv.org75 citations
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Scale and translation-invariance for novel objects in human vision
Yena Han, G. Roig, G. Geiger, T. Poggio
2020Scientific Reports57 citations Open access
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Biologically Inspired Mechanisms for Adversarial Robustness
M. V. Reddy, Andrzej Banburski, Nishka Pant, T. Poggio
2020Neural Information Processing Systems53 citations
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A fast, invariant representation for human action in the visual system.
Leyla Isik, Andrea Tacchetti, T. Poggio
2018Journal of Neurophysiology49 citations
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Symmetry-adapted representation learning
Fabio Anselmi, Georgios Evangelopoulos, L. Rosasco, T. Poggio
2019Pattern Recognition48 citations Open access
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Explicit regularization and implicit bias in deep network classifiers trained with the square loss
T. Poggio, Q. Liao
2020arXiv.org45 citations
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Invariant Recognition Shapes Neural Representations of Visual Input.
Andrea Tacchetti, Leyla Isik, T. Poggio
2018Annual Review of Vision Science43 citations Open access
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Complexity control by gradient descent in deep networks
T. Poggio, Q. Liao, Andrzej Banburski
2020Nature Communications41 citations Open access
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Dynamics in Deep Classifiers Trained with the Square Loss: Normalization, Low Rank, Neural Collapse, and Generalization Bounds
Mengjia Xu, Akshay Rangamani, Q. Liao, Tomer Galanti +1 more
2023Research35 citations Open access
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A Surprising Linear Relationship Predicts Test Performance in Deep Networks
Q. Liao, B. Miranda, Andrzej Banburski, J. Hidary +1 more
2018arXiv.org34 citations
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Double descent in the condition number
T. Poggio, Gil Kur, Andrzej Banburski
2019arXiv.org33 citations
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Approximate inference with Wasserstein gradient flows
Charlie Frogner, T. Poggio
2018International Conference on Artificial Intelligence and Statistics33 citations
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Neural-guided, Bidirectional Program Search for Abstraction and Reasoning
Simon Alford, Anshul Gandhi, Akshay Rangamani, Andrzej Banburski +5 more
2021International Workshop on Complex Networks & Their Applications31 citations Open access
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Theoretical Issues in Deep Networks : Approximation , Optimization and Generalization
T. Poggio, Andrzej Banburski, Q. Liao
201929 citations
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Theory IIIb: Generalization in Deep Networks
T. Poggio, Q. Liao, B. Miranda, Andrzej Banburski +2 more
2018arXiv.org28 citations
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Deep Recurrent Architectures for Seismic Tomography
Amir Adler, M. Araya-Polo, T. Poggio
201981st EAGE Conference and Exhibition 201926 citations Open access
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Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision
Francis X. Chen, G. Roig, Leyla Isik, X. Boix +1 more
2017AAAI Spring Symposia25 citations
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Compression of Deep Neural Networks for Image Instance Retrieval
V. Chandrasekhar, Jie Lin, Q. Liao, Olivier Morère +3 more
2017Data Compression Conference25 citations
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Theory I: Deep networks and the curse of dimensionality
T. Poggio, Q. Liao
2023Bulletin of the Polish Academy of Sciences: Technical Sciences24 citations Open access
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Theory of Deep Learning III : the non-overfitting puzzle
T. Poggio, K. Kawaguchi, Q. Liao, B. Miranda +4 more
201823 citations
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SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks
Tomer Galanti, T. Poggio
2022arXiv.org22 citations Open access
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An analysis of training and generalization errors in shallow and deep networks
H. Mhaskar, T. Poggio
2018Neural Networks22 citations Open access
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Theory of Deep Learning III : Generalization Properties of SGD by
Chiyuan Zhang, Q. Liao, A. Rakhlin, B. Miranda +2 more
201720 citations
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System identification of neural systems: If we got it right, would we know?
Yena Han, T. Poggio, Brian Cheung
2023International Conference on Machine Learning19 citations Open access
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SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Tomer Galanti, Zachary S. Siegel, A. Gupte, T. Poggio
202218 citations
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Generalization in deep network classifiers trained with the square loss
T. Poggio, Q. Liao
202016 citations
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For Interpolating Kernel Machines, Minimizing the Norm of the ERM Solution Maximizes Stability
Akshay Rangamani, L. Rosasco, T. Poggio
2020Analysis and Applications15 citations
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Deep Learning: Theory and Practice
A. Cichocki, T. Poggio, Stanislaw Osowski, V. Lempitsky
201815 citations Open access
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Memo No . 067 June 27 , 2017 Theory of Deep Learning III : Generalization Properties of SGD
Chiyuan Zhang, Q. Liao, A. Rakhlin, B. Miranda +2 more
201715 citations
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Musings on Deep Learning: Properties of SGD
Chiyuan Zhang, Q. Liao, A. Rakhlin, Karthik Sridharan +3 more
201715 citations
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Fast and Flexible Inference of Joint Distributions from their Marginals
Charlie Frogner, T. Poggio
2019International Conference on Machine Learning14 citations
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Theory III: Dynamics and Generalization in Deep Networks1
Andrzej Banburski, Q. Liao, B. Miranda, T. Poggio +4 more
201913 citations
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Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results
Luke A. Arend, Yena Han, Martin Schrimpf, Pouya Bashivan +4 more
201813 citations
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How Deep Sparse Networks Avoid the Curse of Dimensionality: Efﬁciently Computable Functions are Compositionally Sparse
T. Poggio
202312 citations
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Implicit dynamic regularization in deep networks
T. Poggio, Q. Liao
202012 citations
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Norm-based Generalization Bounds for Compositionally Sparse Neural Networks
Tomer Galanti, Mengjia Xu, Liane Galanti, T. Poggio
2023arXiv.org11 citations Open access
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Object-Oriented Deep Learning
Q. Liao, T. Poggio
2017DSpace@MIT (Massachusetts Institute of Technology)11 citations Open access
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Loss landscape: SGD has a better view
T. Poggio, Y. Cooper
202010 citations
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Dreaming with ARC
Andrzej Banburski, Anshul Gandhi, Simon Alford, Sylee Dandekar +2 more
202010 citations
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Loss landscape: SGD can have a better view than GD
T. Poggio, Y. Cooper
20209 citations
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Theory II: Deep learning and optimization
T. Poggio, Q. Liao
2023Bulletin of the Polish Academy of Sciences: Technical Sciences8 citations Open access
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CUDA-Optimized real-time rendering of a Foveated Visual System
Elian Malkin, Arturo Deza, T. Poggio
2020arXiv.org8 citations
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Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex
Jim Mutch, Fabio Anselmi, Andrea Tacchetti, L. Rosasco +2 more
20177 citations Open access
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