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Found 10 papers, 4 papers with code
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks
COCKATIEL is a novel, post-hoc, concept-based, model-agnostic XAI technique that generates meaningful explanations from the last layer of a neural net model trained on an NLP classification task by using Non-Negative Matrix Factorization (NMF) to discover the concepts the model leverages to make predictions and by exploiting a Sensitivity Analysis to estimate accurately the importance of each of these concepts for the model.
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?
To anticipate the certification of recommendation systems using textual data, we then used it on the Bios dataset, for which the learning task consists in predicting the occupation of female and male individuals, based on their LinkedIn biography.
p$^3$VAE: a physics-integrated generative model. Application to the semantic segmentation of optical remote sensing images
Our experiments on a simulated data set demonstrated the benefits of our hybrid model against conventional machine learning models in terms of extrapolation capabilities and interpretability.
A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis
Contrarily to societal applications where a set of proxy variables can be provided by the common sense or by regulations to draw the attention on potential risks, industrial and safety-critical applications are most of the times sailing blind.
Transport-based Counterfactual Models
We address the problem of designing realistic and feasible counterfactuals in the absence of a causal model.
A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world.
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization
We then introduce in this paper a new method to temper the algorithmic bias in Neural-Network based classifiers.
Explaining Machine Learning Models using Entropic Variable Projection
In order to emphasize the impact of each input variable, this formalism uses an information theory framework that quantifies the influence of all input-output observations based on entropic projections.
COREclust: a new package for a robust and scalable analysis of complex data
In this paper, we present a new R package COREclust dedicated to the detection of representative variables in high dimensional spaces with a potentially limited number of observations.
Anisotropic Diffusion in ITK
Anisotropic Non-Linear Diffusion is a powerful image processing technique, which allows to simultaneously remove the noise and enhance sharp features in two or three dimensional images.
