7 citations · 18 across the 5 of their papers we have counts for
5 papers
Comprehensive Deadlock Prevention for GPU Collective Communication
Lichen Pan, Juncheng Liu, Yongquan Fu +4
Distributed deep neural network training necessitates efficient GPU collective communications, which are inherently susceptible to deadlocks. GPU collective deadlocks arise easily…
Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning
Rongkai Zhang, Cong Zhang, Zhiguang Cao +5
We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP wi…
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration
Chong Wang, Rongkai Zhang, Saiprasad Ravishankar +1
Image restoration schemes based on the pre-trained deep models have received great attention due to their unique flexibility for solving various inverse problems. In particular, th…
ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement
Rongkai Zhang, Lanqing Guo, Siyu Huang +1
Low-light image enhancement (LLIE) is a pervasive yet challenging problem, since: 1) low-light measurements may vary due to different imaging conditions in practice; 2) images can…
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery
Rongkai Zhang, Jiang Zhu, Zhiyuan Zha +2
State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training. Alternatively, very recent works utilize deep reinforcement learning for…