5 citations · 9 across the 4 of their papers we have counts for
7 papers
Distributed Ensembles of Reinforcement Learning Agents for Electricity Control
Pierrick Pochelu, Serge G. Petiton, Bruno Conche
Deep Reinforcement Learning (or just "RL") is gaining popularity for industrial and research applications. However, it still suffers from some key limits slowing down its widesprea…
Weakly Supervised Faster-RCNN+FPN to classify animals in camera trap images
Pierrick Pochelu, Clara Erard, Philippe Cordier +2
Camera traps have revolutionized the animal research of many species that were previously nearly impossible to observe due to their habitat or behavior. They are cameras generally…
An efficient and flexible inference system for serving heterogeneous ensembles of deep neural networks
Pierrick Pochelu, Serge G. Petiton, Bruno Conche
Ensembles of Deep Neural Networks (DNNs) have achieved qualitative predictions but they are computing and memory intensive. Therefore, the demand is growing to make them answer a h…
A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters
Pierrick Pochelu, Serge G. Petiton, Bruno Conche
Automated Machine Learning with ensembling (or AutoML with ensembling) seeks to automatically build ensembles of Deep Neural Networks (DNNs) to achieve qualitative predictions. Ens…
Ranking Viscous Finger Simulations to an Acquired Ground Truth with Topology-aware Matchings
Maxime Soler, Martin Petitfrere, Gilles Darche +3
This application paper presents a novel framework based on topological data analysis for the automatic evaluation and ranking of viscous finger simulation runs in an ensemble with…
Lifted Wasserstein Matcher for Fast and Robust Topology Tracking
Maxime Soler, Mélanie Plainchault, Bruno Conche +1
This paper presents a robust and efficient method for tracking topological features in time-varying scalar data. Structures are tracked based on the optimal matching between persis…
Topologically Controlled Lossy Compression
Maxime Soler, Melanie Plainchault, Bruno Conche +1
This paper presents a new algorithm for the lossy compression of scalar data defined on 2D or 3D regular grids, with topological control. Certain techniques allow users to control…