Performance Analysis
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
We shed light on the current progress of GAD, setting a robust groundwork for subsequent investigations in this domain.
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense Kalpesh Krishna Yixiao Song Marzena Karpinska John Wieting Mohit Iyyer
API, looking for sequences that match the candidate text within a certain threshold.
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
Equipped with the knowledge of sample difficulty, we further propose to use it for regularizing the prediction confidence.
4eb33c53ed5b14ce9028309431f565cc-Paper-Datasets_and_Benchmarks.pdf
Does progress on ImageNet transfer to real-world datasets?
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
Learning and Deep Graph Neural Networks to learn gene representations from single-cell sequencing and spatial transcriptomic data.