David J. Schwab

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 33

Top Topics

Supervised Learning

Statistical Mechanics

Information Bottleneck

Top Venues

Publications

Daksh Idnani, Vivek Madan, Naman Goyal, David J. Schwab, Ramakrishna Vedantam
Don't forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure. ICLR (2023)
Vudtiwat Ngampruetikorn, David J. Schwab
Random-Energy Secret Sharing via Extreme Synergy. CoRR (2023)
Vudtiwat Ngampruetikorn, David J. Schwab
Generalized Information Bottleneck for Gaussian Variables. CoRR (2023)
Vudtiwat Ngampruetikorn, David J. Schwab
Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality. CoRR (2022)
Vudtiwat Ngampruetikorn, David J. Schwab
Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality. NeurIPS (2022)
Vudtiwat Ngampruetikorn, David J. Schwab
Perturbation Theory for the Information Bottleneck. CoRR (2021)
Vudtiwat Ngampruetikorn, David J. Schwab
Perturbation Theory for the Information Bottleneck. NeurIPS (2021)
Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos
Leveraging background augmentations to encourage semantic focus in self-supervised contrastive learning. CoRR (2021)
Jonathan Frankle, David J. Schwab, Ari S. Morcos
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs. ICLR (2021)
Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers. NeurIPS (2021)
Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam
Learning Optimal Representations with the Decodable Information Bottleneck. NeurIPS (2020)
Jonathan Frankle, David J. Schwab, Ari S. Morcos
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs. CoRR (2020)
Tankut Can, Kamesh Krishnamurthy, David J. Schwab
Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs. CoRR (2020)
Kamesh Krishnamurthy, Tankut Can, David J. Schwab
Theory of gating in recurrent neural networks. CoRR (2020)
Weishun Zhong, Zhiyue Lu, David J. Schwab, Arvind Murugan
Nonequilibrium Statistical Mechanics of Continuous Attractors. Neural Comput. 32 (6) (2020)
Tankut Can, Kamesh Krishnamurthy, David J. Schwab
Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs. MSML (2020)
Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam
Learning Optimal Representations with the Decodable Information Bottleneck. CoRR (2020)
Jonathan Frankle, David J. Schwab, Ari S. Morcos
The Early Phase of Neural Network Training. ICLR (2020)
Jonathan Frankle, David J. Schwab, Ari S. Morcos
The Early Phase of Neural Network Training. CoRR (2020)
Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab, Ari S. Morcos
Are all negatives created equal in contrastive instance discrimination? CoRR (2020)
Mingwei Wei, David J. Schwab
How noise affects the Hessian spectrum in overparameterized neural networks. CoRR (2019)
Mingwei Wei, James Stokes, David J. Schwab
Mean-field Analysis of Batch Normalization. CoRR (2019)
DJ Strouse, David J. Schwab
The Information Bottleneck and Geometric Clustering. Neural Comput. 31 (3) (2019)
Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew Botvinick, David J. Schwab
Learning to Share and Hide Intentions using Information Regularization. NeurIPS (2018)
DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew Botvinick, David J. Schwab
Learning to Share and Hide Intentions using Information Regularization. CoRR (2018)
Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G. R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab
A high-bias, low-variance introduction to Machine Learning for physicists. CoRR (2018)
DJ Strouse, David J. Schwab
The information bottleneck and geometric clustering. CoRR (2017)
DJ Strouse, David J. Schwab
The Deterministic Information Bottleneck. Neural Comput. 29 (6) (2017)
DJ Strouse, David J. Schwab
The deterministic information bottleneck. CoRR (2016)
David J. Schwab, Pankaj Mehta
Comment on "Why does deep and cheap learning work so well?" [arXiv: 1608.08225]. CoRR (2016)
E. Miles Stoudenmire, David J. Schwab
Supervised Learning with Quantum-Inspired Tensor Networks. CoRR (2016)
Edwin Miles Stoudenmire, David J. Schwab
Supervised Learning with Tensor Networks. NIPS (2016)
DJ Strouse, David J. Schwab
The Deterministic Information Bottleneck. UAI (2016)
Anne-Florence Bitbol, David J. Schwab
Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes. PLoS Comput. Biol. 10 (8) (2014)
Pankaj Mehta, David J. Schwab
An exact mapping between the Variational Renormalization Group and Deep Learning. CoRR (2014)