Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction

no code implementations • 16 Jun 2023 • Shuai Xiao, Chen Pan, Min Wang, Xinxin Zhu, Siqiao Xue, Jing Wang, Yunhua Hu, James Zhang, Jinghua Feng

To this end, we formulate the problem as a partially observable Markov decision problem (POMDP) and employ an environment correction algorithm based on the characteristics of the business.

Hierarchical Reinforcement Learning reinforcement-learning

Explored An Effective Methodology for Fine-Grained Snake Recognition

1 code implementation • 24 Jul 2022 • Yong Huang, Aderon Huang, Wei Zhu, Yanming Fang, Jinghua Feng

Then, in order to take full advantage of unlabeled datasets, we use self-supervised learning and supervised learning joint training to provide pre-trained model.

Fine-Grained Image Classification Self-Supervised Learning

Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN

1 code implementation • 30 Jun 2022 • Kuan Li, Yang Liu, Xiang Ao, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He

However, both strategies are faced with some immediate problems: raw features cannot represent various properties of nodes (e. g., structure information), and representations learned by supervised GNN may suffer from the poor performance of the classifier on the poisoned graph.

Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection

1 code implementation • The Web Conference 2021 • Yang Liu1, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He

Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters.

Fraud Detection Node Classification