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Jonathan Crabbé
Jonathan Crabbé
Member of Technical Staff @ Latent Labs
Vahvistettu sähköpostiosoite verkkotunnuksessa latentlabs.com - Kotisivu
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Mattergen: a generative model for inorganic materials design
C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ...
arXiv preprint arXiv:2312.03687, 2023
1212023
Explaining Time Series Predictions with Dynamic Masks
J Crabbé, M van der Schaar
Proceedings of the 38th International Conference on Machine Learning, 2021
1042021
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
J Crabbé, M van der Schaar
Proceedings of the 36th International Conference on Neural Information …, 2022
612022
Explaining Latent Representations with a Corpus of Examples
J Crabbé, Z Qian, F Imrie, M van der Schaar
Proceedings of the 35th International Conference on Neural Information …, 2021
472021
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
N Seedat, J Crabbé, I Bica, M van der Schaar
Proceedings of the 36th International Conference on Neural Information …, 2022
342022
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
A Jeffares, T Liu, J Crabbé, F Imrie, M van der Schaar
The Eleventh International Conference on Learning Representations, 2023
332023
Learning outside the Black-Box: The pursuit of interpretable models
J Crabbe, Y Zhang, W Zame, M van der Schaar
Proceedings of the 34th International Conference on Neural Information …, 2020
312020
Label-Free Explainability for Unsupervised Models
J Crabbé, M van der Schaar
Proceedings of the 39th International Conference on Machine Learning, 2022
272022
A generative model for inorganic materials design
C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, Z Wang, ...
Nature, 1-3, 2025
232025
Joint Training of Deep Ensembles Fails Due to Learner Collusion
A Jeffares, T Liu, J Crabbé, M van der Schaar
Proceedings of the 37th International Conference on Neural Information …, 2023
222023
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
J Crabbé, A Curth, I Bica, M van der Schaar
Proceedings of the Neural Information Processing Systems Track on Datasets …, 2022
212022
Data-SUITE: Data-centric identification of in-distribution incongruous examples
N Seedat, J Crabbe, M van der Schaar
Proceedings of the 39th International Conference on Machine Learning, 2022
212022
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization
H Sun, B van Breugel, J Crabbé, N Seedat, M van der Schaar
Proceedings of the 37th International Conference on Neural Information …, 2023
12*2023
MatterGen: a generative model for inorganic materials design.(2023)
C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ...
arXiv preprint arXiv:2312.03687, 2023
112023
MatterGen: a generative model for inorganic materials design, arXiv, 2024
C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ...
arXiv preprint arXiv:2312.03687 10, 0
9
Time series diffusion in the frequency domain
J Crabbé, N Huynh, J Stanczuk, M van der Schaar
Proceedings of the 41st International Conference on Machine Learning, 2024
82024
TRIAGE: Characterizing and auditing training data for improved regression
N Seedat, J Crabbé, Z Qian, M van der Schaar
Proceedings of the 37th International Conference on Neural Information …, 2023
82023
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
J Crabbé, M van der Schaar
Proceedings of the 37th International Conference on Neural Information …, 2023
82023
DAGnosis: Localized identification of data inconsistencies using structures
N Huynh, J Berrevoets, N Seedat, J Crabbé, Z Qian, M van der Schaar
International Conference on Artificial Intelligence and Statistics, 1864-1872, 2024
32024
MatterGen: a generative model for inorganic materials design, arXiv
C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ...
Preprint.] Jan 29, 2024
32024
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Artikkelit 1–20