Approches variationnelles régularisées pour la résolution de problèmes inverses et pour l'apprentissage machine : de la ...
An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs
The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On th...
Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical...
Published in Journal of Medical Internet Research
Background Recently, artificial intelligence technologies and machine learning methods have offered attractive prospects to design and manage crisis response processes, especially in suicide crisis management. In other domains, most algorithms are based on big data to help diagnose and suggest rational treatment options in medicine. But data in psy...
Security in the Internet of Things: Recent Challenges and Solutions
International audience
Identifying Benchmarks for Failure Prediction in Industry 4.0
Industry 4.0 is characterized by the availability of sensors to operate the so-called intelligent factory. Predictive maintenance, in particular, failure prediction, is an important issue to cut the costs associated with production breaks. We studied more than 40 publications on predictive maintenance. We point out that they focus on various machin...
An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists
Topological Data Analysis (TDA)is a recent and fast growing field providing a set of new topological and geometric tools to infer relevant features for possibly complex data. This paper is a brief introduction, through a few selected topics, to basic fundamental and practical aspects of TDA for non experts.
Learning with Partially Labeled Data for Multi-class Classification and Feature Selection
Learning with partially labeled data, known as semi-supervised learning, deals with problems where few training examples are labeled while available unlabeled data are abundant and valuable for training. In this thesis, we study this framework in the multi-class classification case with a focus on self-learning and feature selection. Self-learning ...
Learning with Partially Labeled Data for Multi-class Classification and Feature Selection
Learning with partially labeled data, known as semi-supervised learning, deals with problems where few training examples are labeled while available unlabeled data are abundant and valuable for training. In this thesis, we study this framework in the multi-class classification case with a focus on self-learning and feature selection. Self-learning ...
Adaptive methods for optimization in stochastic environments
Imagine that we have access to a simulator that models the behaviour of some complex numerical task. Being considered as a black box, we can only get useful information by running the simulator with different inputs. For example, the process of inferring the 3D structure of a protein from its amino-acid sequence can be regarded as such a complex ta...
Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks
We make three related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC-Bayesian setting: (1) we show how averaging over an ensemble of stochastic neural networks enables a new class of \emph{partially-aggregated} estimators; (2) we show that these lead to provably lower-variance gradient estimat...
