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Found 7 papers, 3 papers with code
Layerwise Early Stopping for Test Time Adaptation
In this paper, we propose Layerwise EArly STopping (LEAST) for TTA to address this problem.
GROOD: GRadient-aware Out-Of-Distribution detection in interpolated manifolds
To build this space, GROOD relies on class prototypes together with a prototype that specifically captures OOD characteristics.
Understanding Interventional TreeSHAP : How and Why it Works
Shapley values are ubiquitous in interpretable Machine Learning due to their strong theoretical background and efficient implementation in the SHAP library.
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set
In this work, instead of aiming at reducing the under-specification of model explanations, we fully embrace it and extract logical statements about feature attributions that are consistent across all models with good empirical performance (i. e. all models in the Rashomon Set).
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
Method: We conduct a Systematic Literature Review (SLR) of research papers published between 2015 to 2020, covering topics related to the certification of ML systems.
Out-of-distribution detection for regression tasks: parameter versus predictor entropy
Focusing on regression tasks, we choose a simple yet insightful model for this OOD distribution and conduct an empirical evaluation of the ability of various methods to discriminate OOD samples from the data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
Embeddability on functions: order and chaos
Our main result is the following dichotomy: the embeddability quasi-order restricted to continuous functions from a given compact space to another is either an analytic complete quasi-order or a well-quasi-order.
Logic Primary: 03E15, 26A21, 54C05, 54C25, Secondary: 06A07
