Výsledky vyhledávání - "Daniel Haziza"

VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation

Autor: Daniel Haziza, Changhan Wang, Morgane Riviere, Juan Pino, Anne Wu, Ann B. Lee, Mary Williamson, Chaitanya Talnikar, Emmanuel Dupoux

Publikováno v: 2021, ⟨10.18653/v1/2021.acl-long.80⟩
ACL/IJCNLP (1)
ACL 2021-59th Annual Meeting of the Association for Computational Linguistics
ACL 2021-59th Annual Meeting of the Association for Computational Linguistics, Aug 2021, Bangkok, Thailand

We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31dab65f924fddb10ab121a9ba5bccc4

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Low Bandwidth Video-Chat Compression using Deep Generative Models

Autor: Daniel Haziza, Thibault Peyronel, Peizhao Zhang, Maxime Oquab, Oran Gafni, Tao Xu, Patrick Labatut, Camille Couprie, Yana Hasson, Onur Celebi, Bobo Bose-Kolanu, Pierre Stock

Publikováno v: CVPR Workshops

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57e17ee435fcc8a5dab3a35202f36fac
http://arxiv.org/abs/2012.00328

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open source evolutionary structured optimization

Autor: Pauline Bennet, Olivier Teytaud, Emmanuel Centeno, Daniel Haziza, Antoine Moreau, Jeremy Rapin

Publikováno v: Genetic and evolutionary computation conference companion
Genetic and evolutionary computation conference companion, Jul 2020, Cancun, Mexico. pp.1599
GECCO Companion
HAL

Nevergrad is a derivative-free optimization platform gathering both a wide range of optimization methods and a wide range of test functions to evaluate them upon. Some of these functions have very particular structures which standard methods are not

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::010ddbf8f876092ec650c70f02668aba
https://hal.uca.fr/hal-03086426

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Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning

Autor: Alexandre M. Bayen, Gabriel Gomes, Daniel Haziza, Francois Belletti

Publikováno v: IEEE Transactions on Intelligent Transportation Systems. 19:1198-1207

This paper shows how the recent breakthroughs in reinforcement learning (RL) that have enabled robots to learn to play arcade video games, walk, or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineer

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bebc2975fc6ee776c2085088782cd4a
https://doi.org/10.1109/tits.2017.2725912

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