no code implementations • 14 Mar 2024 • 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 language.
1 code implementation • 9 Oct 2022 • Pierrick Pochelu
This paper takes a holistic approach to conduct an empirical comparison and analysis of four representative DL inference frameworks.
no code implementations • 30 Aug 2022 • Pierrick Pochelu, Clara Erard, Philippe Cordier, Serge G. Petiton, Bruno Conche
First, it automatically performs the weakly-supervised bounding box annotation using the motion from multiple frames.
no code implementations • 30 Aug 2022 • Pierrick Pochelu, Serge G. Petiton, Bruno Conche
Deep Reinforcement Learning (or just "RL") is gaining popularity for industrial and research applications.
no code implementations • 30 Aug 2022 • Pierrick Pochelu, Serge G. Petiton, Bruno Conche
Experiments show the flexibility and efficiency under extreme scenarios: It successes to serve an ensemble of 12 heavy DNNs into 4 GPUs and at the opposite, one single DNN multi-threaded into 16 GPUs.
no code implementations • 30 Aug 2022 • Pierrick Pochelu, Serge G. Petiton, Bruno Conche
Finally, we propose a novel algorithm to optimize the inference of the DNNs ensemble in a GPU cluster based on allocation optimization.
no code implementations • 29 Sep 2021 • Pierrick Pochelu, Serge G. Petiton, Bruno Conche
Automated Machine Learning with ensembling seeks to automatically build ensembles of Deep Neural Networks (DNNs) to achieve qualitative predictions.
no code implementations • 1 Jan 2021 • Pierrick Pochelu, Bruno Conche, Serge G. Petiton
Due to the lack of consensus to design a successful deep learning ensemble, we introduce Hyperband-Dijkstra, a new workflow that automatically explores neural network designs with Hyperband and efficiently combines them with Dijkstra's algorithm.