most citedA Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters

5 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.PL2024

JaxDecompiler: Redefining Gradient-Informed Software Design

Pierrick Pochelu

Among numerical libraries capable of computing gradient descent optimization, JAX stands out by offering more features, accelerated by an intermediate representation known as Jaxpr…

cs.LG2022

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…

cs.CV20224 cited

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…

cs.DC2022

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…

cs.LG20225 cited

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…