20 citations · 22 across the 5 of their papers we have counts for
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cs.LG2020★ 20 cited
Better Fine-Tuning by Reducing Representational Collapse
Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta +3
Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on t…
cs.LG2019★ 1 cited
SURREAL-System: Fully-Integrated Stack for Distributed Deep Reinforcement Learning
Linxi Fan, Yuke Zhu, Jiren Zhu +6
We present an overview of SURREAL-System, a reproducible, flexible, and scalable framework for distributed reinforcement learning (RL). The framework consists of a stack of four la…