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ORCIDDavid J. Schwab
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- affiliation: Northwestern University, Evanston, Department of Physics
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2020 – today
- 2024
[i21]Alex Nguyen, David J. Schwab, Vudtiwat Ngampruetikorn:
Generalization vs. Specialization under Concept Shift. CoRR abs/2409.15582 (2024)- 2023
- [c11]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 - [i20]Vudtiwat Ngampruetikorn
, David J. Schwab:
Generalized Information Bottleneck for Gaussian Variables. CoRR abs/2303.17762 (2023) - [i19]Vudtiwat Ngampruetikorn, David J. Schwab:
Random-Energy Secret Sharing via Extreme Synergy. CoRR abs/2309.14047 (2023) - 2022
- [c10]Vudtiwat Ngampruetikorn, David J. Schwab:
Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality. NeurIPS 2022 - [i18]Vudtiwat Ngampruetikorn, David J. Schwab:
Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality. CoRR abs/2208.03848 (2022) - 2021
- [c9]Jonathan Frankle, David J. Schwab, Ari S. Morcos:
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs. ICLR 2021 - [c8]Vudtiwat Ngampruetikorn, David J. Schwab:
Perturbation Theory for the Information Bottleneck. NeurIPS 2021: 21008-21018 - [c7]Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab:
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers. NeurIPS 2021: 28131-28143 - [i17]Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos:
Leveraging background augmentations to encourage semantic focus in self-supervised contrastive learning. CoRR abs/2103.12719 (2021) - [i16]Vudtiwat Ngampruetikorn, David J. Schwab:
Perturbation Theory for the Information Bottleneck. CoRR abs/2105.13977 (2021) - 2020
- [j4]Weishun Zhong, Zhiyue Lu, David J. Schwab, Arvind Murugan:
Nonequilibrium Statistical Mechanics of Continuous Attractors. Neural Comput. 32(6): 1033-1068 (2020) - [c6]Jonathan Frankle, David J. Schwab, Ari S. Morcos:
The Early Phase of Neural Network Training. ICLR 2020 - [c5]Tankut Can, Kamesh Krishnamurthy, David J. Schwab:
Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs. MSML 2020: 476-511 - [c4]Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam:
Learning Optimal Representations with the Decodable Information Bottleneck. NeurIPS 2020 - [i15]Tankut Can, Kamesh Krishnamurthy, David J. Schwab:
Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs. CoRR abs/2002.00025 (2020) - [i14]Jonathan Frankle, David J. Schwab, Ari S. Morcos:
The Early Phase of Neural Network Training. CoRR abs/2002.10365 (2020) - [i13]Jonathan Frankle, David J. Schwab, Ari S. Morcos:
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs. CoRR abs/2003.00152 (2020) - [i12]Kamesh Krishnamurthy, Tankut Can, David J. Schwab:
Theory of gating in recurrent neural networks. CoRR abs/2007.14823 (2020) - [i11]Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam:
Learning Optimal Representations with the Decodable Information Bottleneck. CoRR abs/2009.12789 (2020) - [i10]Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab, Ari S. Morcos:
Are all negatives created equal in contrastive instance discrimination? CoRR abs/2010.06682 (2020)
2010 – 2019
- 2019
- [j3]DJ Strouse, David J. Schwab:
The Information Bottleneck and Geometric Clustering. Neural Comput. 31(3) (2019) - [i9]Mingwei Wei, James Stokes, David J. Schwab:
Mean-field Analysis of Batch Normalization. CoRR abs/1903.02606 (2019) - [i8]Mingwei Wei, David J. Schwab:
How noise affects the Hessian spectrum in overparameterized neural networks. CoRR abs/1910.00195 (2019) - 2018
- [c3]Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew M. Botvinick, David J. Schwab:
Learning to Share and Hide Intentions using Information Regularization. NeurIPS 2018: 10270-10281 - [i7]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 abs/1803.08823 (2018) - [i6]DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew M. Botvinick, David J. Schwab:
Learning to Share and Hide Intentions using Information Regularization. CoRR abs/1808.02093 (2018) - 2017
- [j2]DJ Strouse, David J. Schwab:
The Deterministic Information Bottleneck. Neural Comput. 29(6): 1611-1630 (2017) - [i5]DJ Strouse, David J. Schwab:
The information bottleneck and geometric clustering. CoRR abs/1712.09657 (2017) - 2016
- [c2]Edwin Miles Stoudenmire, David J. Schwab:
Supervised Learning with Tensor Networks. NIPS 2016: 4799-4807 - [c1]DJ Strouse, David J. Schwab:
The Deterministic Information Bottleneck. UAI 2016 - [i4]DJ Strouse, David J. Schwab:
The deterministic information bottleneck. CoRR abs/1604.00268 (2016) - [i3]E. Miles Stoudenmire, David J. Schwab:
Supervised Learning with Quantum-Inspired Tensor Networks. CoRR abs/1605.05775 (2016) - [i2]David J. Schwab, Pankaj Mehta:
Comment on "Why does deep and cheap learning work so well?" [arXiv: 1608.08225]. CoRR abs/1609.03541 (2016) - 2014
- [j1]Anne-Florence Bitbol, David J. Schwab:
Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes. PLoS Comput. Biol. 10(8) (2014) - [i1]Pankaj Mehta, David J. Schwab:
An exact mapping between the Variational Renormalization Group and Deep Learning. CoRR abs/1410.3831 (2014)
Coauthor Index
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