73 citations · 159 across the 12 of their papers we have counts for
16 papers
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
Lanqing Li, Liang Zeng, Ziqi Gao +11
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD). However, real-world pharmaceutical datasets…
Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method
Lu Zhang, Yang Wang, Jiaogen Zhou +5
Few-shot object detection (FSOD) is to detect objects with a few examples. However, existing FSOD methods do not consider hierarchical fine-grained category structures of objects t…
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup
Bingzhe Wu, Zhipeng Liang, Yuxuan Han +3
Recently, federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. Nevertheless, di…
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport
Jiying Zhang, Xi Xiao, Long-Kai Huang +2
Recently, the pretrain-finetuning paradigm has attracted tons of attention in graph learning community due to its power of alleviating the lack of labels problem in many real-world…
Transformer for Graphs: An Overview from Architecture Perspective
Erxue Min, Runfa Chen, Yatao Bian +7
Recently, Transformer model, which has achieved great success in many artificial intelligence fields, has demonstrated its great potential in modeling graph-structured data. Till n…