1 code implementation • 24 Mar 2024 • Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, Bo Long
Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations.
1 code implementation • NeurIPS 2021 • Runzhong Wang, Zhigang Hua, Gan Liu, Jiayi Zhang, Junchi Yan, Feng Qi, Shuang Yang, Jun Zhou, Xiaokang Yang
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature.
no code implementations • 5 Mar 2021 • Zhigang Hua, Feng Qi, Gan Liu, Shuang Yang
Scheduling computational tasks represented by directed acyclic graphs (DAGs) is challenging because of its complexity.
no code implementations • 10 Jun 2020 • Jian Du, Zhigang Hua, Shuang Yang
We examine the \emph{submodular maximum coverage problem} (SMCP), which is related to a wide range of applications.
no code implementations • 2 Feb 2020 • Xingwen Zhang, Feng Qi, Zhigang Hua, Shuang Yang
Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale.