5 citations · 12 across the 7 of their papers we have counts for
7 papers
What Artificial Intelligence can do for High-Performance Computing systems?
Pierrick Pochelu, Hyacinthe Cartiaux, Julien Schleich
High-performance computing (HPC) centers consume substantial power, incurring environmental and operational costs. This review assesses how artificial intelligence (AI), including…
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…
Deep Learning Inference Frameworks Benchmark
Pierrick Pochelu
Deep learning (DL) has been widely adopted those last years but they are computing-intensive method. Therefore, scientists proposed diverse optimization to accelerate their predict…
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…