39 citations · 83 across the 6 of their papers we have counts for
6 papers
T-Rep: Representation Learning for Time Series using Time-Embeddings
Archibald Fraikin, Adrien Bennetot, Stéphanie Allassonnière
Multivariate time series present challenges to standard machine learning techniques, as they are often unlabeled, high dimensional, noisy, and contain missing data. To address this…
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification
Adrien Bennetot, Gianni Franchi, Javier Del Ser +2
Although Deep Neural Networks (DNNs) have great generalization and prediction capabilities, their functioning does not allow a detailed explanation of their behavior. Opaque deep l…
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications
Adrien Bennetot, Ivan Donadello, Ayoub El Qadi +10
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and pred…
Should artificial agents ask for help in human-robot collaborative problem-solving?
Adrien Bennetot, Vicky Charisi, Natalia Díaz-Rodríguez
Transferring as fast as possible the functioning of our brain to artificial intelligence is an ambitious goal that would help advance the state of the art in AI and robotics. It is…
Towards Explainable Neural-Symbolic Visual Reasoning
Adrien Bennetot, Jean-Luc Laurent, Raja Chatila +1
Many high-performance models suffer from a lack of interpretability. There has been an increasing influx of work on explainable artificial intelligence (XAI) in order to disentangl…
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser +9
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this t…