6 citations · 18 across the 13 of their papers we have counts for
3 papers · 1 filter
GRP-Obliteration: Unaligning LLMs With a Single Unlabeled Prompt
Mark Russinovich, Yanan Cai, Keegan Hines +3
Safety alignment is only as robust as its weakest failure mode. Despite extensive work on safety post-training, it has been shown that models can be readily unaligned through post-…
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
Raphaël Pellegrin, Blake Bullwinkel, Marios Mattheakis +1
Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sci…
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks
Blake Bullwinkel, Dylan Randle, Pavlos Protopapas +1
Solutions to differential equations are of significant scientific and engineering relevance. Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving…