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Crabbé, Jonathan

  1. MatterGen: a generative model for inorganic materials design
    2023/12/06 by Claudio Zeni, Zeni, Claudio, Robert Pinsler +41 · 1 voice · 21 citations
    Computer Science · Engineering · Materials Science · Physics and Astronomy · #Machine Learning in Materials Science #Modular Robots and Swarm Intelligence #cond-mat.mtrl-sci #cs.AI
  2. Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
    2022/09/22 by Crabbé, Jonathan, van der Schaar, Mihaela · 9 citations
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  3. Explaining Time Series Predictions with Dynamic Masks
    2021/06/09 by Jonathan Crabbé, Mihaela van der Schaar, Crabbé, Jonathan +1 · 7 citations
    Computer Science · #Explainable Artificial Intelligence (XAI) #Machine Learning in Healthcare #Time Series Analysis and Forecasting
  4. Time Series Diffusion in the Frequency Domain
    2024/02/08 by Crabbé, Jonathan, Huynh, Nicolas, Stanczuk, Jan +1 · 10 citations
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  5. Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
    2022/06/16 by Jonathan Crabbé, Alicia Curth, Crabbé, Jonathan +5 · 6 citations
    Mathematics · Medicine · Computer Science · #Advanced Causal Inference Techniques #Radiomics and Machine Learning in Medical Imaging #Machine Learning in Healthcare
  6. Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
    2023/04/13 by Jonathan Crabbé, Crabbé, Jonathan, Mihaela van der Schaar +1 · 5 citations
    Computer Science · Materials Science · #Explainable Artificial Intelligence (XAI) #Adversarial Robustness in Machine Learning #Machine Learning in Materials Science
  7. Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
    2022/10/24 by Seedat, Nabeel, Crabbé, Jonathan, Bica, Ioana +1 · 5 citations
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  8. TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
    2023/03/09 by Jeffares, Alan, Liu, Tennison, Crabbé, Jonathan +2 · 4 citations
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  9. Joint Training of Deep Ensembles Fails Due to Learner Collusion
    2023/01/26 by Alan Jeffares, Tennison Liu, Jeffares, Alan +5 · 2 citations
    Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification
  10. Explaining Latent Representations with a Corpus of Examples
    2021/10/28 by Crabbé, Jonathan, Qian, Zhaozhi, Imrie, Fergus +1 · 1 citation
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG)
  11. Data-SUITE: Data-centric identification of in-distribution incongruous examples
    2022/02/17 by Nabeel Seedat, Jonathan Crabbé, Seedat, Nabeel +3 · 1 citation
    Computer Science · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Machine Learning in Healthcare
  12. Label-Free Explainability for Unsupervised Models
    2022/03/03 by Crabbé, Jonathan, van der Schaar, Mihaela · 1 citation
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  13. Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design
    2025/07/25 by Latent Labs Team, Bridgland, Alex, Crabbé, Jonathan +13 · 4 citations
    #Biomolecules (q-bio.BM) #FOS: Biological sciences
  14. LaTable: Towards Large Tabular Models
    2024/06/25 by van Breugel, Boris, Crabbé, Jonathan, Davis, Rob +1 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG)