33 citations · 48 across the 3 of their papers we have counts for
3 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★ 5 cited
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…
cs.AI2017★ 33 cited
Beating Atari with Natural Language Guided Reinforcement Learning
Russell Kaplan, Christopher Sauer, Alexander Sosa
We introduce the first deep reinforcement learning agent that learns to beat Atari games with the aid of natural language instructions. The agent uses a multimodal embedding betwee…