1 citations · 1 across the 10 of their papers we have counts for
12 papers
ReliableRAG: Combating Misinformation in Retrieval-Augmented Generation via Reliability-Guided Reasoning Chains
Jinpu Jiang, Xuan Wu, Wenhao Song +6
Retrieval-Augmented Generation (RAG) has emerged as a powerful architecture for Question Answering (QA) by integrating external information into Large Language Models (LLMs). Howev…
WeCon: An Efficient Weight-Conditioned Neural Solver for Multi-Objective Combinatorial Optimization Problems
Xuan Wu, Jinbiao Chen, Yang Li +7
Existing neural solvers for Multi-Objective Combinatorial Optimization Problems (MOCOPs) commonly adopt decomposition-based strategies that scalarize a MOCOP into multiple subprobl…
Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
Jiaqi Wang, Zhiguang Cao, Peng Zhao +4
The rise of smart manufacturing under Industry 4.0 introduces mass customization and dynamic production, demanding more advanced and flexible scheduling techniques. The flexible jo…
iPEAR: Iterative Pyramid Estimation with Attention and Residuals for Deformable Medical Image Registration
Heming Wu, Di Wang, Tai Ma +6
Existing pyramid registration networks may accumulate anatomical misalignments and lack an effective mechanism to dynamically determine the number of optimization iterations under…
Efficient Neural Combinatorial Optimization Solver for the Min-max Heterogeneous Capacitated Vehicle Routing Problem
Xuan Wu, Di Wang, Chunguo Wu +5
Numerous Neural Combinatorial Optimization (NCO) solvers have been proposed to address Vehicle Routing Problems (VRPs). However, most of these solvers focus exclusively on single-v…
Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution
Wenhao Song, Xuan Wu, Bo Yang +6
To address the weight coupling problem, certain studies introduced few-shot Neural Architecture Search (NAS) methods, which partition the supernet into multiple sub-supernets. Howe…
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language Models
Xuan Wu, Di Wang, Chunguo Wu +4
Recent studies exploited Large Language Models (LLMs) to autonomously generate heuristics for solving Combinatorial Optimization Problems (COPs), by prompting LLMs to first provide…
GELD: A Unified Neural Model for Efficiently Solving Traveling Salesman Problems Across Different Scales
Yubin Xiao, Di Wang, Rui Cao +3
The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recent advancements in neural network-based TSP solvers…
An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem
Mingzhao Wang, You Zhou, Zhiguang Cao +7
Recent advances in neural models have shown considerable promise in solving Traveling Salesman Problems (TSPs) without relying on much hand-crafted engineering. However, while non-…
Improving Generalization of Neural Vehicle Routing Problem Solvers Through the Lens of Model Architecture
Yubin Xiao, Di Wang, Xuan Wu +5
Neural models produce promising results when solving Vehicle Routing Problems (VRPs), but often fall short in generalization. Recent attempts to enhance model generalization often…
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives
Xuan Wu, Di Wang, Lijie Wen +6
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically designed to solve Vehicle Routing Problems (VRPs) have been conducted, they did not cover t…
Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed
Yubin Xiao, Di Wang, Boyang Li +4
Neural construction models have shown promising performance for Vehicle Routing Problems (VRPs) by adopting either the Autoregressive (AR) or Non-Autoregressive (NAR) learning appr…