133 citations · 510 across the 36 of their papers we have counts for
8 papers · 1 filter
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning
Hang Xu, Xinghua Qu, Zinovi Rabinovich
This paper investigates policy resilience to training-environment poisoning attacks on reinforcement learning (RL) policies, with the goal of recovering the deployment performance…
Revisiting Over-smoothing in BERT from the Perspective of Graph
Han Shi, Jiahui Gao, Hang Xu +5
Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields. However, no existing work has delved deeper to further investigate th…
BWCP: Probabilistic Learning-to-Prune Channels for ConvNets via Batch Whitening
Wenqi Shao, Hang Yu, Zhaoyang Zhang +3
This work presents a probabilistic channel pruning method to accelerate Convolutional Neural Networks (CNNs). Previous pruning methods often zero out unimportant channels in traini…
Effective Sparsification of Neural Networks with Global Sparsity Constraint
Xiao Zhou, Weizhong Zhang, Hang Xu +1
Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments. However, since magnitudes and relative impo…
DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning
Kelly Kostopoulou, Hang Xu, Aritra Dutta +3
Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification proc…
SparseBERT: Rethinking the Importance Analysis in Self-attention
Han Shi, Jiahui Gao, Xiaozhe Ren +4
Transformer-based models are popularly used in natural language processing (NLP). Its core component, self-attention, has aroused widespread interest. To understand the self-attent…
OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research
Kai Li, Hang Xu, Enmin Zhao +2
Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed…
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
Han Shi, Renjie Pi, Hang Xu +3
Neural Architecture Search (NAS) has shown great potentials in finding better neural network designs. Sample-based NAS is the most reliable approach which aims at exploring the sea…