9 citations · 16 across the 18 of their papers we have counts for
8 papers · 1 filter
EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample
Guohao Chen, Shuaicheng Niu, Geng Li +4
Test-time model evolution offers a promising way for deployed models to improve from unlabeled test-time experience, yet most existing methods depend on backpropagation (BP), which…
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…
Graph Topology Information Enhanced Heterogeneous Graph Representation Learning
He Zhao, Zhiwei Zeng, Yongwei Wang +1
Real-world heterogeneous graphs are inherently noisy and usually not in the optimal graph structures for downstream tasks, which often adversely affects the performance of GRL mode…
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification
Tian Tian, Chunyan Miao, Hangwei Qian
Contrastive learning has emerged as a competent approach for unsupervised representation learning. However, the design of an optimal augmentation strategy, although crucial for con…
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning
Xiaoqi Qiu, Yongjie Wang, Xu Guo +4
Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classe…
HGAttack: Transferable Heterogeneous Graph Adversarial Attack
He Zhao, Zhiwei Zeng, Yongwei Wang +2
Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce, where resilience against adversarial attacks is…
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning
Chenyu Sun, Hangwei Qian, Chunyan Miao
Offline reinforcement learning (RL) aims to learn an effective policy from a pre-collected dataset. Most existing works are to develop sophisticated learning algorithms, with less…
Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations
Yongjie Wang, Hangwei Qian, Yongjie Liu +2
Counterfactual explanations (CFEs) exemplify how to minimally modify a feature vector to achieve a different prediction for an instance. CFEs can enhance informational fairness and…