480 citations · 724 across the 18 of their papers we have counts for
6 papers · 1 filter
Attention Meets Post-hoc Interpretability: A Mathematical Perspective
Gianluigi Lopardo, Frederic Precioso, Damien Garreau
Attention-based architectures, in particular transformers, are at the heart of a technological revolution. Interestingly, in addition to helping obtain state-of-the-art results on…
Understanding Post-hoc Explainers: The Case of Anchors
Gianluigi Lopardo, Frederic Precioso, Damien Garreau
In many scenarios, the interpretability of machine learning models is a highly required but difficult task. To explain the individual predictions of such models, local model-agnost…
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron +3
Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters. While…
Generalised Mutual Information for Discriminative Clustering
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron +4
In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regul…
A Sea of Words: An In-Depth Analysis of Anchors for Text Data
Gianluigi Lopardo, Frederic Precioso, Damien Garreau
Anchors (Ribeiro et al., 2018) is a post-hoc, rule-based interpretability method. For text data, it proposes to explain a decision by highlighting a small set of words (an anchor)…
From text saliency to linguistic objects: learning linguistic interpretable markers with a multi-channels convolutional architecture
Laurent Vanni, Marco Corneli, Damon Mayaffre +1
A lot of effort is currently made to provide methods to analyze and understand deep neural network impressive performances for tasks such as image or text classification. These met…