18 citations · 26 across the 10 of their papers we have counts for
7 papers · 1 filter
Efficient Training of Multi-task Neural Solver for Combinatorial Optimization
Chenguang Wang, Zhang-Hua Fu, Pinyan Lu +1
Efficiently training a multi-task neural solver for various combinatorial optimization problems (COPs) has been less studied so far. Naive application of conventional multi-task le…
ASP: Learn a Universal Neural Solver!
Chenguang Wang, Zhouliang Yu, Stephen McAleer +2
Applying machine learning to combinatorial optimization problems has the potential to improve both efficiency and accuracy. However, existing learning-based solvers often struggle…
Learning to Decouple Complex Systems
Zihan Zhou, Tianshu Yu
A complex system with cluttered observations may be a coupled mixture of multiple simple sub-systems corresponding to latent entities. Such sub-systems may hold distinct dynamics i…
W2SAT: Learning to generate SAT instances from Weighted Literal Incidence Graphs
Weihuang Wen, Tianshu Yu
The Boolean Satisfiability (SAT) problem stands out as an attractive NP-complete problem in theoretic computer science and plays a central role in a broad spectrum of computing-rel…
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features
Shanchao Yang, Kaili Ma, Baoxiang Wang +2
Improving the resilience of a network is a fundamental problem in network science, which protects the underlying system from natural disasters and malicious attacks. This is tradit…
Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
Runzhong Wang, Tianqi Zhang, Tianshu Yu +2
Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Tra…