mlsanityData to 7 Oct 2026

GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts

arXiv 2310.08677 ↗First posted 12 Oct 2023cs.LG cs.AIcited by 4

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Abstract (arXiv)

Geometric deep learning (GDL) has gained significant attention in scientific fields, for its proficiency in modeling data with intricate geometric structures. However, very few works have delved into its capability of tackling the distribution shift problem, a prevalent challenge in many applications. To bridge this gap, we propose GeSS, a comprehensive benchmark designed for evaluating the performance of GDL models in scientific scenarios with distribution shifts. Our evaluation datasets cover diverse scientific domains from particle physics, materials science to biochemistry, and encapsulate a broad spectrum of distribution shifts including conditional, covariate, and concept shifts. Furthermore, we study three levels of information access from the out-of-distribution (OOD) test data, including no OOD information, only unlabeled OOD data, and OOD data with a few labels. Overall, our benchmark results in 30 different experiment settings, and evaluates 3 GDL backbones and 11 learning algorithms in each setting. A thorough analysis of the evaluation results is provided, poised to illuminate insights for GDL researchers and domain practitioners who are to use GDL in their applications.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 66 of its 117 references that are in the map. First posted on arXiv 12 Oct 2023.

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66 of 117 references.

References · 66

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  3. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  4. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  5. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  7. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,842
  8. 2019Invariant Risk Minimizationcited by 1,832
  9. 2020Point Transformercited by 1,462
  10. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  11. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
  12. 2020In Search of Lost Domain Generalizationcited by 1,042
38 more
  1. 2021E(n) Equivariant Graph Neural Networkscited by 951
  2. 2021Robust fine-tuning of zero-shot modelscited by 785
  3. 2020Out-of-Distribution Generalization via Risk Extrapolation (REx)cited by 737
  4. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  5. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  6. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  7. 2020Directional Message Passing for Molecular Graphscited by 556
  8. 2020Learning under Concept Drift: A Reviewcited by 470
  9. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  10. 2020Learning from Protein Structure with Geometric Vector Perceptronscited by 308
  11. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 307
  12. 2020Environment Inference for Invariant Learningcited by 306
  13. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  14. 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
  15. 2018Explicit Inductive Bias for Transfer Learning with Convolutional Networkscited by 226
  16. 2021Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalizationcited by 220
  17. 2022Surgical Fine-Tuning Improves Adaptation to Distribution Shiftscited by 205
  18. 2022Improving Out-of-Distribution Robustness via Selective Augmentationcited by 202
  19. 2022Discovering Invariant Rationales for Graph Neural Networkscited by 186
  20. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  21. 2021A Fine-Grained Analysis on Distribution Shiftcited by 171
  22. 2021Spherical Message Passing for 3D Graph Networkscited by 166
  23. 2020BREEDS: Benchmarks for Subpopulation Shiftcited by 155
  24. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  25. 2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 140
  26. 2020Invariant Rationalizationcited by 131
  27. 2023Change is Hard: A Closer Look at Subpopulation Shiftcited by 119
  28. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Modelscited by 97
  29. 2023Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluationscited by 96
  30. 2022The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalystscited by 95
  31. 2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
  32. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 85
  33. 2022Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Timecited by 85
  34. 2022GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspectivecited by 72
  35. 2022ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphscited by 69
  36. 2022ZIN: When and How to Learn Invariance Without Environment Partition?cited by 58
  37. 2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
  38. 2022IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languagescited by 54

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

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