Markov Transitions between Attractor States in a Recurrent Neural Network. - Jeremy Bernstein; Ishita Dasgupta 001; David Rolnick; Haim Sompolinsky (AAAI Spring Symposia, 2017)

Compression by the signs: distributed learning is a two-way street. - Jeremy Bernstein; Yu-Xiang Wang 003; Kamyar Azizzadenesheli; Anima Anandkumar (ICLR (Workshop), 2018)

Stochastic Activation Pruning for Robust Adversarial Defense. - Guneet S. Dhillon; Kamyar Azizzadenesheli; Zachary C. Lipton; Jeremy Bernstein; Jean Kossaifi; Aran Khanna; Animashree Anandkumar (ICLR (Poster), 2018)

SIGNSGD: Compressed Optimisation for Non-Convex Problems. - Jeremy Bernstein; Yu-Xiang Wang 003; Kamyar Azizzadenesheli; Animashree Anandkumar (ICML, 2018)

signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant. - Jeremy Bernstein; Jiawei Zhao; Kamyar Azizzadenesheli; Anima Anandkumar (CoRR, 2018)

signSGD with Majority Vote is Communication Efficient and Fault Tolerant. - Jeremy Bernstein; Jiawei Zhao; Kamyar Azizzadenesheli; Anima Anandkumar (ICLR (Poster), 2019)

On the distance between two neural networks and the stability of learning. - Jeremy Bernstein; Arash Vahdat; Yisong Yue; Ming-Yu Liu 001 (NeurIPS, 2020)

Learning compositional functions via multiplicative weight updates. - Jeremy Bernstein; Jiawei Zhao; Markus Meister; Ming-Yu Liu 001; Anima Anandkumar; Yisong Yue (NeurIPS, 2020)

Learning by Turning: Neural Architecture Aware Optimisation. - Yang Liu; Jeremy Bernstein; Markus Meister; Yisong Yue (ICML, 2021)

Fine-Grained System Identification of Nonlinear Neural Circuits. - Dawna Bagherian; James Gornet; Jeremy Bernstein; Yu-Li Ni; Yisong Yue; Markus Meister (KDD, 2021)

Computing the Information Content of Trained Neural Networks. - Jeremy Bernstein; Yisong Yue (CoRR, 2021)

On the Implicit Biases of Architecture & Gradient Descent. - Jeremy Bernstein; Yisong Yue (CoRR, 2021)

Investigating Generalization by Controlling Normalized Margin. - Alexander R. Farhang; Jeremy D. Bernstein; Kushal Tirumala; Yang Liu; Yisong Yue (ICML, 2022)

Optimisation & Generalisation in Networks of Neurons. - Jeremy Bernstein (CoRR, 2022)

Automatic Gradient Descent: Deep Learning without Hyperparameters. - Jeremy Bernstein; Chris Mingard; Kevin Huang; Navid Azizan; Yisong Yue (CoRR, 2023)

SketchOGD: Memory-Efficient Continual Learning. - Benjamin Wright; Youngjae Min; Jeremy Bernstein; Navid Azizan (CoRR, 2023)

Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models. - Fredrik Heiding; Bruce Schneier; Arun Vishwanath; Jeremy Bernstein (CoRR, 2023)

A Spectral Condition for Feature Learning. - Greg Yang; James B. Simon; Jeremy Bernstein (CoRR, 2023)

Scalable Optimization in the Modular Norm. - Tim Large; Yang Liu; Jacob Huh; Hyojin Bahng; Phillip Isola; Jeremy Bernstein (NeurIPS, 2024)

Devising and Detecting Phishing Emails Using Large Language Models. - Fredrik Heiding; Bruce Schneier; Arun Vishwanath; Jeremy Bernstein; Peter S. Park (IEEE Access, 2024)

Training Neural Networks from Scratch with Parallel Low-Rank Adapters. - Minyoung Huh; Brian Cheung; Jeremy Bernstein; Phillip Isola; Pulkit Agrawal 001 (CoRR, 2024)

Scalable Optimization in the Modular Norm. - Tim Large; Yang Liu; Minyoung Huh; Hyojin Bahng; Phillip Isola; Jeremy Bernstein (CoRR, 2024)

Old Optimizer, New Norm: An Anthology. - Jeremy Bernstein; Laker Newhouse (CoRR, 2024)

Modular Duality in Deep Learning. - Jeremy Bernstein; Laker Newhouse (CoRR, 2024)

Training Transformers with Enforced Lipschitz Constants. - Laker Newhouse; R. Preston Hess; Franz Cesista; Andrii Zahorodnii; Jeremy Bernstein; Phillip Isola (CoRR, 2025)