David J. Schwab

According to our database, David J. Schwab authored at least 26 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Generalization vs. Specialization under Concept Shift.

Alex Nguyen, David J. Schwab, Vudtiwat Ngampruetikorn

CoRR, 2024

2023

Random-Energy Secret Sharing via Extreme Synergy.

Vudtiwat Ngampruetikorn, David J. Schwab

CoRR, 2023

Generalized Information Bottleneck for Gaussian Variables.

Vudtiwat Ngampruetikorn, David J. Schwab

CoRR, 2023

Don't forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure.

Daksh Idnani, Vivek Madan, Naman Goyal, David J. Schwab, Ramakrishna Vedantam

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality.

Vudtiwat Ngampruetikorn, David J. Schwab

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Leveraging background augmentations to encourage semantic focus in self-supervised contrastive learning.

Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos

CoRR, 2021

An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers.

Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Perturbation Theory for the Information Bottleneck.

Vudtiwat Ngampruetikorn, David J. Schwab

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs.

Jonathan Frankle, David J. Schwab, Ari S. Morcos

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Nonequilibrium Statistical Mechanics of Continuous Attractors.

Weishun Zhong, Zhiyue Lu, David J. Schwab, Arvind Murugan

Neural Comput., 2020

Are all negatives created equal in contrastive instance discrimination?

Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab, Ari S. Morcos

CoRR, 2020

Theory of gating in recurrent neural networks.

Kamesh Krishnamurthy, Tankut Can, David J. Schwab

CoRR, 2020

Learning Optimal Representations with the Decodable Information Bottleneck.

Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs.

Tankut Can, Kamesh Krishnamurthy, David J. Schwab

Proceedings of Mathematical and Scientific Machine Learning, 2020

The Early Phase of Neural Network Training.

Jonathan Frankle, David J. Schwab, Ari S. Morcos

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

The Information Bottleneck and Geometric Clustering.

DJ Strouse, David J. Schwab

Neural Comput., 2019

How noise affects the Hessian spectrum in overparameterized neural networks.

Mingwei Wei, David J. Schwab

CoRR, 2019

Mean-field Analysis of Batch Normalization.

Mingwei Wei, James Stokes, David J. Schwab

CoRR, 2019

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

Learning to Share and Hide Intentions using Information Regularization.

Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew M. Botvinick, David J. Schwab

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

The Deterministic Information Bottleneck.

DJ Strouse, David J. Schwab

Neural Comput., 2017

2016

Supervised Learning with Quantum-Inspired Tensor Networks.

E. Miles Stoudenmire, David J. Schwab

CoRR, 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 Tensor Networks.

Edwin Miles Stoudenmire, David J. Schwab

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2014

Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes.

Anne-Florence Bitbol, David J. Schwab

PLoS Comput. Biol., 2014

An exact mapping between the Variational Renormalization Group and Deep Learning.

Pankaj Mehta, David J. Schwab

CoRR, 2014