An exact mapping between the Variational Renormalization Group and Deep Learning. - Pankaj Mehta; David J. Schwab (CoRR, 2014)

Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes. - Anne-Florence Bitbol; David J. Schwab (PLoS Comput. Biol., 2014)

Supervised Learning with Tensor Networks. - Edwin Miles Stoudenmire; David J. Schwab (NIPS, 2016)

Comment on "Why does deep and cheap learning work so well?" [arXiv: 1608.08225]. - David J. Schwab; Pankaj Mehta (CoRR, 2016)

Supervised Learning with Quantum-Inspired Tensor Networks. - E. Miles Stoudenmire; David J. Schwab (CoRR, 2016)

The Deterministic Information Bottleneck. - DJ Strouse; David J. Schwab (Neural Comput., 2017)

Learning to Share and Hide Intentions using Information Regularization. - Daniel Strouse; Max Kleiman-Weiner; Josh Tenenbaum 001; Matthew M. Botvinick; David J. Schwab (NeurIPS, 2018)

A high-bias, low-variance introduction to Machine Learning for physicists. - Pankaj Mehta; Marin Bukov; Ching-Hao Wang; Alexandre G. R. Day; Clint Richardson; Charles K. Fisher; David J. Schwab (CoRR, 2018)

Mean-field Analysis of Batch Normalization. - Mingwei Wei; James Stokes; David J. Schwab (CoRR, 2019)

How noise affects the Hessian spectrum in overparameterized neural networks. - Mingwei Wei; David J. Schwab (CoRR, 2019)

The Information Bottleneck and Geometric Clustering. - DJ Strouse; David J. Schwab (Neural Comput., 2019)

The Early Phase of Neural Network Training. - Jonathan Frankle; David J. Schwab; Ari S. Morcos (ICLR, 2020)

Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs. - Tankut Can; Kamesh Krishnamurthy; David J. Schwab (MSML, 2020)

Learning Optimal Representations with the Decodable Information Bottleneck. - Yann Dubois; Douwe Kiela; David J. Schwab; Ramakrishna Vedantam (NeurIPS, 2020)

Theory of gating in recurrent neural networks. - Kamesh Krishnamurthy; Tankut Can; David J. Schwab (CoRR, 2020)

Are all negatives created equal in contrastive instance discrimination? - Tiffany Tianhui Cai; Jonathan Frankle; David J. Schwab; Ari S. Morcos (CoRR, 2020)

Nonequilibrium Statistical Mechanics of Continuous Attractors. - Weishun Zhong; Zhiyue Lu; David J. Schwab; Arvind Murugan (Neural Comput., 2020)

Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs. - Jonathan Frankle; David J. Schwab; Ari S. Morcos (ICLR, 2021)

Perturbation Theory for the Information Bottleneck. - Vudtiwat Ngampruetikorn; David J. Schwab (NeurIPS, 2021)

An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers. - Ramakrishna Vedantam; David Lopez-Paz; David J. Schwab (NeurIPS, 2021)

Leveraging background augmentations to encourage semantic focus in self-supervised contrastive learning. - Chaitanya K. Ryali; David J. Schwab; Ari S. Morcos (CoRR, 2021)

Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality. - Vudtiwat Ngampruetikorn; David J. Schwab (NeurIPS, 2022)

Don't forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure. - Daksh Idnani; Vivek Madan; Naman Goyal 001; David J. Schwab; Ramakrishna Vedantam (ICLR, 2023)

Generalized Information Bottleneck for Gaussian Variables. - Vudtiwat Ngampruetikorn; David J. Schwab (CoRR, 2023)

Random-Energy Secret Sharing via Extreme Synergy. - Vudtiwat Ngampruetikorn; David J. Schwab (CoRR, 2023)

Generalization vs. Specialization under Concept Shift. - Alex Nguyen; David J. Schwab; Vudtiwat Ngampruetikorn (CoRR, 2024)

When can in-context learning generalize out of task distribution? - Page C. Goddard; Lindsay M. Smith; Vudtiwat Ngampruetikorn; David J. Schwab (CoRR, 2025)