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05/25/2023
DP-SGD Without Clipping: The Lipschitz Neural Network Way
State-of-the-art approaches for training Differentially Private (DP) Dee...
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11/17/2022
CRAFT: Concept Recursive Activation FacTorization for Explainability
Attribution methods are a popular class of explainability methods that u...
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10/13/2022
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning
Quantum walks on binary trees are used in many quantum algorithms to ach...
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06/13/2022
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
This paper presents a new efficient black-box attribution method based o...
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06/09/2022
Xplique: A Deep Learning Explainability Toolbox
Today's most advanced machine-learning models are hardly scrutable. The ...
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02/15/2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis
A variety of methods have been proposed to try to explain how deep neura...
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11/07/2021
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
We describe a novel attribution method which is grounded in Sensitivity ...
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09/07/2020
Representativity and Consistency Measures for Deep Neural Network Explanations
The adoption of machine learning in critical contexts requires a reliabl...
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11/05/2018
