A curated list of trustworthy deep learning papers. Daily updating...
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Updated
Jun 12, 2024
A curated list of trustworthy deep learning papers. Daily updating...
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
[ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability (ICCV 2023)
GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
Implementation codes for NeurIPS23 paper "Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts"
The Pytorch implementation for "Are Data-driven Explanations Robust against Out-of-distribution Data?" (CVPR 2023)
The implementation of "Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization" (NeurIPS 2023)
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
Code for the research paper Meta-learning with hierarchical models based on similarity of causal mechanisms
[NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization
[NeurIPS 2022] The official repository of Expression Learning with Identity Matching for Facial Expression Recognition
[NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
Official PyTorch implementation of the ICCV'23 paper “Anomaly Detection under Distribution Shift”
[NeurIPS 2023] “SODA: Robust Training of Test-Time Data Adaptors”
[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
Papers about out-of-distribution generalization on graphs.
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