Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Altché, Florent"'
Autor:
Jarrett, Daniel, Tallec, Corentin, Altché, Florent, Mesnard, Thomas, Munos, Rémi, Valko, Michal
Publikováno v:
In Proc. 40th International Conference on Machine Learning (ICML 2023)
Consider the problem of exploration in sparse-reward or reward-free environments, such as in Montezuma's Revenge. In the curiosity-driven paradigm, the agent is rewarded for how much each realized outcome differs from their predicted outcome. But usi
Externí odkaz:
http://arxiv.org/abs/2211.10515
Autor:
Strudel, Robin, Tallec, Corentin, Altché, Florent, Du, Yilun, Ganin, Yaroslav, Mensch, Arthur, Grathwohl, Will, Savinov, Nikolay, Dieleman, Sander, Sifre, Laurent, Leblond, Rémi
Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can simply project tokens in a continuous space of embeddings, as is standar
Externí odkaz:
http://arxiv.org/abs/2211.04236
Autor:
Guo, Zhaohan Daniel, Thakoor, Shantanu, Pîslar, Miruna, Pires, Bernardo Avila, Altché, Florent, Tallec, Corentin, Saade, Alaa, Calandriello, Daniele, Grill, Jean-Bastien, Tang, Yunhao, Valko, Michal, Munos, Rémi, Azar, Mohammad Gheshlaghi, Piot, Bilal
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments. BYOL-Explore learns a world representation, the world dynamics, and an exploration policy all-together by optimizin
Externí odkaz:
http://arxiv.org/abs/2206.08332
Autor:
Recasens, Adrià, Luc, Pauline, Alayrac, Jean-Baptiste, Wang, Luyu, Hemsley, Ross, Strub, Florian, Tallec, Corentin, Malinowski, Mateusz, Patraucean, Viorica, Altché, Florent, Valko, Michal, Grill, Jean-Bastien, Oord, Aäron van den, Zisserman, Andrew
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by image techniques, where these two views are similarly extracted by cro
Externí odkaz:
http://arxiv.org/abs/2103.16559
Autor:
Richemond, Pierre H., Grill, Jean-Bastien, Altché, Florent, Tallec, Corentin, Strub, Florian, Brock, Andrew, Smith, Samuel, De, Soham, Pascanu, Razvan, Piot, Bilal, Valko, Michal
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation. From an augmented view of an image, BYOL trains an online network to predict a target network representation of a different augmented view of the same i
Externí odkaz:
http://arxiv.org/abs/2010.10241
Autor:
Grill, Jean-Bastien, Altché, Florent, Tang, Yunhao, Hubert, Thomas, Valko, Michal, Antonoglou, Ioannis, Munos, Rémi
The combination of Monte-Carlo tree search (MCTS) with deep reinforcement learning has led to significant advances in artificial intelligence. However, AlphaZero, the current state-of-the-art MCTS algorithm, still relies on handcrafted heuristics tha
Externí odkaz:
http://arxiv.org/abs/2007.12509
Autor:
Grill, Jean-Bastien, Strub, Florian, Altché, Florent, Tallec, Corentin, Richemond, Pierre H., Buchatskaya, Elena, Doersch, Carl, Pires, Bernardo Avila, Guo, Zhaohan Daniel, Azar, Mohammad Gheshlaghi, Piot, Bilal, Kavukcuoglu, Koray, Munos, Rémi, Valko, Michal
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented
Externí odkaz:
http://arxiv.org/abs/2006.07733
Autor:
Guo, Daniel, Pires, Bernardo Avila, Piot, Bilal, Grill, Jean-bastien, Altché, Florent, Munos, Rémi, Azar, Mohammad Gheshlaghi
Learning a good representation is an essential component for deep reinforcement learning (RL). Representation learning is especially important in multitask and partially observable settings where building a representation of the unknown environment i
Externí odkaz:
http://arxiv.org/abs/2004.14646
Autor:
Altché, Florent
Le déploiement des futurs véhicules autonomes promet d'avoir un impact socio-économique majeur, en raison de leur promesse d'être à la fois plus sûrs et plus efficaces que ceux conduits par des humains. Afin de satisfaire à ces attentes, la ca
Externí odkaz:
http://www.theses.fr/2018PSLEM061/document
Autor:
Azar, Mohammad Gheshlaghi, Piot, Bilal, Pires, Bernardo Avila, Grill, Jean-Bastien, Altché, Florent, Munos, Rémi
As humans we are driven by a strong desire for seeking novelty in our world. Also upon observing a novel pattern we are capable of refining our understanding of the world based on the new information---humans can discover their world. The outstanding
Externí odkaz:
http://arxiv.org/abs/1902.07685