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Found 9 papers, 5 papers with code
DP-SGD Without Clipping: The Lipschitz Neural Network Way
To provide sensitivity bounds and bypass the drawbacks of the clipping process, we propose to rely on Lipschitz constrained networks.
CRAFT: Concept Recursive Activation FacTorization for Explainability
However, recent research has exposed the limited practical value of these methods, attributed in part to their narrow focus on the most prominent regions of an image -- revealing "where" the model looks, but failing to elucidate "what" the model sees in those areas.
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 achieve important speedup over classical algorithms.
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
HSIC measures the dependence between regions of an input image and the output of a model based on kernel embeddings of distributions.
Xplique: A Deep Learning Explainability Toolbox
Today's most advanced machine-learning models are hardly scrutable.
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 neural networks make their decisions.
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices.
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks
A plethora of methods have been proposed to explain how deep neural networks reach their decisions but comparatively, little effort has been made to ensure that the explanations produced by these methods are objectively relevant.
FUNN: Flexible Unsupervised Neural Network
We propose a method to obtain robust features in unsupervised learning tasks against adversarial attacks.
