Jonathan Crabbé
Jonathan Crabbé
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Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models
Over the past decade, Deep Learning (DL) models have proven to be efficient at classifying remotely sensed Earth Observation (EO) …
Arthur Vandenhoeke
,
Lennert Antson
,
Guillem Ballesteros
,
Jonathan Crabbé
,
Michal Shimoni
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TRIAGE: Characterizing and auditing training data for improved regression
TRIAGE provides systematic data characterization for regression settings; compatible with a variety of regressors.
Nabeel Seedat
,
Jonathan Crabbé
,
Zhaozhi Qian
,
Mihaela van der Schaar
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Poster
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
We assess the robustness of various interpretability methods by measuring how their explanations change when applying symmetries of the model to the input features.
Jonathan Crabbé
,
Mihaela van der Schaar
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Poster
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
We introduce TANGOS, a regularization method that orthogonalizes the gradient attribution of neurons to improve the generalization of deep neural networks on tabular data.
Alan Jeffares
,
Tennison Liu
,
Jonathan Crabbé
,
Fergus Imrie
,
Mihaela van der Schaar
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Joint Training of Deep Ensembles Fails Due to Learner Collusion
Training deep ensembles via a shared objective results in degenerate behavior.
Alan Jeffares
,
Tennison Liu
,
Jonathan Crabbé
,
Mihaela van der Schaar
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Project
Poster
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
We extend existing feature and example importance methods to unsupervised learning.
Jonathan Crabbé
,
Mihaela van der Schaar
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Video
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
We introduce Data-IQ, a data-centric framework to identify ambiguous examples.
Nabeel Seedat
,
Jonathan Crabbé
,
Ioana Bica
,
Mihaela van der Schaar
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Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
We benchmark treatment effect models with interpretability tools.
Jonathan Crabbé
,
Alicia Curth
,
Ioana Bica
,
Mihaela van der Schaar
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Project
Label-Free Explainability for Unsupervised Models
We extend existing feature and example importance methods to unsupervised learning.
Jonathan Crabbé
,
Mihaela van der Schaar
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Project
Video
Data-SUITE: Data-centric identification of in-distribution incongruous examples
We introduce Data-SUITE, a data-centric framework to identify incongruous examples.
Nabeel Seedat
,
Jonathan Crabbé
,
Mihaela van der Schaar
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