79 citations · 855 across the 98 of their papers we have counts for
113 papers
Toward Stronger Textual Attack Detectors
Pierre Colombo, Marine Picot, Nathan Noiry +2
The landscape of available textual adversarial attacks keeps growing, posing severe threats and raising concerns regarding the deep NLP system's integrity. However, the crucial pro…
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
Pierre Colombo, Victor Pellegrain, Malik Boudiaf +5
Proprietary and closed APIs are becoming increasingly common to process natural language, and are impacting the practical applications of natural language processing, including few…
A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification
Pierre Colombo, Nathan Noiry, Guillaume Staerman +1
One of the pursued objectives of deep learning is to provide tools that learn abstract representations of reality from the observation of multiple contextual situations. More preci…
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection
Eduardo Dadalto, Pierre Colombo, Guillaume Staerman +2
A key feature of out-of-distribution (OOD) detection is to exploit a trained neural network by extracting statistical patterns and relationships through the multi-layer classifier…
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection
Eduardo Dadalto, Marco Romanelli, Georg Pichler +1
Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable. However, con…
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Federica Granese, Marco Romanelli, Siddharth Garg +1
Multi-armed adversarial attacks, in which multiple algorithms and objective loss functions are simultaneously used at evaluation time, have been shown to be highly successful in fo…