3 citations · 5 across the 5 of their papers we have counts for
5 papers
Training Transformers with Enforced Lipschitz Constants
Laker Newhouse, R. Preston Hess, Franz Cesista +3
Neural networks are often highly sensitive to input and weight perturbations. This sensitivity has been linked to pathologies such as vulnerability to adversarial examples, diverge…
Modular Duality in Deep Learning
Jeremy Bernstein, Laker Newhouse
An old idea in optimization theory says that since the gradient is a dual vector it may not be subtracted from the weights without first being mapped to the primal space where the…
Old Optimizer, New Norm: An Anthology
Jeremy Bernstein, Laker Newhouse
Deep learning optimizers are often motivated through a mix of convex and approximate second-order theory. We select three such methods -- Adam, Shampoo and Prodigy -- and argue tha…
An Assessment of Model-On-Model Deception
Julius Heitkoetter, Michael Gerovitch, Laker Newhouse
The trustworthiness of highly capable language models is put at risk when they are able to produce deceptive outputs. Moreover, when models are vulnerable to deception it undermine…
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Zhuo Chen, Laker Newhouse, Eddie Chen +2
Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology. However, du…