39 citations · 239 across the 55 of their papers we have counts for
5 papers · 1 filter
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward
Weilin Liu, Ye Mu, Chao Yu +6
Discovering hazardous scenarios is crucial in testing and further improving driving policies. However, conducting efficient driving policy testing faces two key challenges. On the…
BoolNet: Minimizing The Energy Consumption of Binary Neural Networks
Nianhui Guo, Joseph Bethge, Haojin Yang +4
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts. However, the accuracy gains are often…
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness
Yi Cai, Xuefei Ning, Huazhong Yang +1
Adversarial attacks have rendered high security risks on modern deep learning systems. Adversarial training can significantly enhance the robustness of neural network models by sup…
Machine Learning for Electronic Design Automation: A Survey
Guyue Huang, Jingbo Hu, Yifan He +13
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in…
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning
Minxue Tang, Xuefei Ning, Yitu Wang +4
Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramaticall…