10 citations · 10 across the 1 of their papers we have counts for
2 papers
cs.CL2023★ 10 cited
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs
George Pu, Anirudh Jain, Jihan Yin +1
As foundation models continue to exponentially scale in size, efficient methods of adaptation become increasingly critical. Parameter-efficient fine-tuning (PEFT), a recent class o…
cs.CV2018
HiDDeN: Hiding Data With Deep Networks
Jiren Zhu, Russell Kaplan, Justin Johnson +1
Recent work has shown that deep neural networks are highly sensitive to tiny perturbations of input images, giving rise to adversarial examples. Though this property is usually con…