84 citations · 152 across the 8 of their papers we have counts for
6 papers · 1 filter
Dataset Growth
Ziheng Qin, Zhaopan Xu, Yukun Zhou +10
Deep learning benefits from the growing abundance of available data. Meanwhile, efficiently dealing with the growing data scale has become a challenge. Data publicly available are…
Switch EMA: A Free Lunch for Better Flatness and Sharpness
Siyuan Li, Zicheng Liu, Juanxi Tian +9
Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DN…
Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning
Hongyu Zang, Xin Li, Leiji Zhang +5
While bisimulation-based approaches hold promise for learning robust state representations for Reinforcement Learning (RL) tasks, their efficacy in offline RL tasks has not been up…
EVNet: An Explainable Deep Network for Dimension Reduction
Zelin Zang, Shenghui Cheng, Linyan Lu +7
Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful propertie…
Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye +5
While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use eith…
Robust Optimization over Multiple Domains
Qi Qian, Shenghuo Zhu, Jiasheng Tang +3
In this work, we study the problem of learning a single model for multiple domains. Unlike the conventional machine learning scenario where each domain can have the corresponding m…