6 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.CV2023
Layer-adaptive Structured Pruning Guided by Latency
Siyuan Pan, Linna Zhang, Jie Zhang +3
Structured pruning can simplify network architecture and improve inference speed. Combined with the underlying hardware and inference engine in which the final model is deployed, b…
cs.LG2022★ 2 cited
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
Wenhui Huang, Cong Zhang, Jingda Wu +3
Massive practical works addressed by Deep Q-network (DQN) algorithm have indicated that stochastic policy, despite its simplicity, is the most frequently used exploration approach.…
stat.ML2020★ 6 cited
Active Sampling for Min-Max Fairness
Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner +3
We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimiza…