Performance Analysis
4eb33c53ed5b14ce9028309431f565cc-Paper-Datasets_and_Benchmarks.pdf
Does progress on ImageNet transfer to real-world datasets?
Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, Balaji Lakshminarayanan
CIFAR-10) and tested on another (e.g.
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.
AHA: Human-Assisted Out-of-Distribution Generalization and Detection
This paper introduces a novel, integrated approach AHA ( A daptive H uman-A ssisted OOD learning) to simultaneously address both OOD generalization and detection through a human-assisted framework by labeling data in the wild.
Average Individual Fairness: Algorithms, Generalization and Experiments
Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth
Neural Information Processing Systems http://nips.cc/