2 papers
cs.LG2025
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…
math.PR2024
How to quantify the coherence of a set of beliefs
Rowan Hess, Lionel Levine
Given conflicting probability estimates for a set of events, how can we quantify how much they conflict? How can we find a single probability distribution that best encapsulates th…