Search Results for author: Hai Wan

Found 21 papers, 4 papers with code

Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum

no code implementations11 Dec 2023 Fan Xu, Nan Wang, Hao Wu, Xuezhi Wen, Xibin Zhao, Hai Wan

This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively.

Binary Classification Fraud Detection

How to Evaluate Semantic Communications for Images with ViTScore Metric?

no code implementations9 Sep 2023 Tingting Zhu, Bo Peng, Jifan Liang, Tingchen Han, Hai Wan, Jingqiao Fu, Junjie Chen

Experimental results demonstrate that ViTScore can better evaluate the image semantic similarity than the other 3 typical metrics, which indicates that ViTScore is an effective performance metric when deployed in SC scenarios.

MS-SSIM Semantic Similarity +2

Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey

no code implementations24 Apr 2023 Chao Yu, Xuejing Zheng, Hankz Hankui Zhuo, Hai Wan, Weilin Luo

Reinforcement Learning(RL) has achieved tremendous development in recent years, but still faces significant obstacles in addressing complex real-life problems due to the issues of poor system generalization, low sample efficiency as well as safety and interpretability concerns.

reinforcement-learning Reinforcement Learning (RL)

A Noise-tolerant Differentiable Learning Approach for Single Occurrence Regular Expression with Interleaving

no code implementations1 Dec 2022 Rongzhen Ye, Tianqu Zhuang, Hai Wan, Jianfeng Du, Weilin Luo, Pingjia Liang

We design a neural network to simulate SOIRE matching and theoretically prove that certain assignments of the set of parameters learnt by the neural network, called faithful encodings, are one-to-one corresponding to SOIREs for a bounded size.

Learning Visual Planning Models from Partially Observed Images

no code implementations25 Nov 2022 Kebing Jin, Zhanhao Xiao, Hankui Hankz Zhuo, Hai Wan, Jiaran Cai

Although a number of approaches have been developed for learning planning models from fully observed unstructured data (e. g., images), in many scenarios raw observations are often incomplete.

Grow and Merge: A Unified Framework for Continuous Categories Discovery

no code implementations9 Oct 2022 Xinwei Zhang, Jianwen Jiang, Yutong Feng, Zhi-Fan Wu, Xibin Zhao, Hai Wan, Mingqian Tang, Rong Jin, Yue Gao

Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories.

Self-Supervised Learning

Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters

no code implementations19 Oct 2021 Kebing Jin, Hankz Hankui Zhuo, Zhanhao Xiao, Hai Wan, Subbarao Kambhampati

In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent.

valid

A DQN-based Approach to Finding Precise Evidences for Fact Verification

1 code implementation ACL 2021 Hai Wan, Haicheng Chen, Jianfeng Du, Weilin Luo, Rongzhen Ye

Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV.

Claim Verification Fact Verification +2

Structural Similarity of Boundary Conditions and an Efficient Local Search Algorithm for Goal Conflict Identification

no code implementations23 Feb 2021 Hongzhen Zhong, Hai Wan, Weilin Luo, Zhanhao Xiao, Jia Li, Biqing Fang

By taking experiments on a set of cases, we show that LOGION effectively exploits the structural similarity of BCs.

Refining HTN Methods via Task Insertion with Preferences

no code implementations29 Nov 2019 Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Andreas Herzig, Laurent Perrussel, Peilin Chen

Hierarchical Task Network (HTN) planning is showing its power in real-world planning.

Representation Learning for Classical Planning from Partially Observed Traces

no code implementations19 Jul 2019 Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Jinxia Lin, Yanan Liu

The experimental results show that the domain models learned by our approach are much more effective on solving real planning problems.

Representation Learning

CoAPI: An Efficient Two-Phase Algorithm Using Core-Guided Over-Approximate Cover for Prime Compilation of Non-Clausal Formulae

no code implementations7 Jun 2019 Weilin Luo, Hai Wan, Hongzhen Zhong, Ou Wei

The state-of-the-art approaches generate all primes along with a prime cover constructed by prime implicates using dual rail encoding.

A General Multi-agent Epistemic Planner Based on Higher-order Belief Change

1 code implementation29 Jun 2018 Xiao Huang, Biqing Fang, Hai Wan, Yongmei Liu

Based on our reasoning, revision and update algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK.

Adversarial Attribute-Image Person Re-identification

no code implementations5 Dec 2017 Zhou Yin, Wei-Shi Zheng, An-Cong Wu, Hong-Xing Yu, Hai Wan, Xiaowei Guo, Feiyue Huang, Jian-Huang Lai

While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extra features or for performing multi-task learning to assist the image-image matching task.

Attribute Multi-Task Learning +1

Query Answering with Inconsistent Existential Rules under Stable Model Semantics

no code implementations18 Feb 2016 Hai Wan, Heng Zhang, Peng Xiao, Haoran Huang, Yan Zhang

Surprisingly, for R-acyclic existential rules with R-stratified or guarded existential rules with stratified negations, both the data complexity and combined complexity of query answering under the rule {repair semantics} remain the same as that under the conventional query answering semantics.

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