107 citations · 135 across the 14 of their papers we have counts for
8 papers · 1 filter
Offline Behavioral Data Selection
Shiye Lei, Zhihao Cheng, Dacheng Tao
Behavioral cloning is a widely adopted approach for offline policy learning from expert demonstrations. However, the large scale of offline behavioral datasets often results in com…
State Diversity Matters in Offline Behavior Distillation
Shiye Lei, Zhihao Cheng, Dacheng Tao
Offline Behavior Distillation (OBD), which condenses massive offline RL data into a compact synthetic behavioral dataset, offers a promising approach for efficient policy training…
Offline Behavior Distillation
Shiye Lei, Sen Zhang, Dacheng Tao
Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficie…
A Comprehensive Survey of Dataset Distillation
Shiye Lei, Dacheng Tao
Deep learning technology has developed unprecedentedly in the last decade and has become the primary choice in many application domains. This progress is mainly attributed to a sys…
Understanding Deep Learning via Decision Boundary
Shiye Lei, Fengxiang He, Yancheng Yuan +1
This paper discovers that the neural network with lower decision boundary (DB) variability has better generalizability. Two new notions, algorithm DB variability and -data…
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer
Shiye Lei, Zhuozhuo Tu, Leszek Rutkowski +4
Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large n…
Spectral Complexity-scaled Generalization Bound of Complex-valued Neural Networks
Haowen Chen, Fengxiang He, Shiye Lei +1
Complex-valued neural networks (CVNNs) have been widely applied to various fields, especially signal processing and image recognition. However, few works focus on the generalizatio…
Neural networks behave as hash encoders: An empirical study
Fengxiang He, Shiye Lei, Jianmin Ji +1
The input space of a neural network with ReLU-like activations is partitioned into multiple linear regions, each corresponding to a specific activation pattern of the included ReLU…