Velodrome combines semi-supervised learning and out-of-distribution generalization (domain generalization) for drug response prediction and pharmacogenomics
-
Updated
Nov 17, 2021 - Python
Velodrome combines semi-supervised learning and out-of-distribution generalization (domain generalization) for drug response prediction and pharmacogenomics
Codes and datasets for NeurIPS21 paper “Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach”
GradCAM-based Copy and Paste Augmentation
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models
This work is a analysis of representations acquired for standard, OOD and Biased data on numerous objective functions.
The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).
The Pytorch implementation for "Topology-aware Robust Optimization for Out-of-Distribution Generalization" (ICLR 2023)
Potential energy ranking for domain generalization (DG)
Papers about out-of-distribution generalization on graphs.
The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
[NeurIPS 2023] “SODA: Robust Training of Test-Time Data Adaptors”
Official PyTorch implementation of the ICCV'23 paper “Anomaly Detection under Distribution Shift”
[NeurIPS 2022] The official repository of Expression Learning with Identity Matching for Facial Expression Recognition
Code for the research paper Meta-learning with hierarchical models based on similarity of causal mechanisms
Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms
The implementation of "Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization" (NeurIPS 2023)
Implementation codes for NeurIPS23 paper "Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts"
Add a description, image, and links to the out-of-distribution-generalization topic page so that developers can more easily learn about it.
To associate your repository with the out-of-distribution-generalization topic, visit your repo's landing page and select "manage topics."