Search Results - "Sifre, Laurent"

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play by Silver, David, Hubert, Thomas, Schrittwieser, Julian, Antonoglou, Ioannis, Lai, Matthew, Guez, Arthur, Lanctot, Marc, Sifre, Laurent, Kumaran, Dharshan, Graepel, Thore, Lillicrap, Timothy, Simonyan, Karen, Hassabis, Demis

“…The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated…”
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Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination by Sifre, Laurent, Mallat, Stephane

“…An affine invariant representation is constructed with a cascade of invariants, which preserves information for classification. A joint translation and…”
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Mastering the game of Go without human knowledge by Silver, David, Schrittwieser, Julian, Simonyan, Karen, Antonoglou, Ioannis, Huang, Aja, Guez, Arthur, Hubert, Thomas, Baker, Lucas, Lai, Matthew, Bolton, Adrian, Chen, Yutian, Lillicrap, Timothy, Hui, Fan, Sifre, Laurent, van den Driessche, George, Graepel, Thore, Hassabis, Demis

“…A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa , superhuman proficiency in challenging domains. Recently, AlphaGo…”
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Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) by Senior, Andrew W., Evans, Richard, Jumper, John, Kirkpatrick, James, Sifre, Laurent, Green, Tim, Qin, Chongli, Žídek, Augustin, Nelson, Alexander W. R., Bridgland, Alex, Penedones, Hugo, Petersen, Stig, Simonyan, Karen, Crossan, Steve, Kohli, Pushmeet, Jones, David T., Silver, David, Kavukcuoglu, Koray, Hassabis, Demis

“…We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13. Submissions were made by three free‐modeling (FM)…”
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Mastering Atari, Go, chess and shogi by planning with a learned model by Schrittwieser, Julian, Antonoglou, Ioannis, Hubert, Thomas, Simonyan, Karen, Sifre, Laurent, Schmitt, Simon, Guez, Arthur, Lockhart, Edward, Hassabis, Demis, Graepel, Thore, Lillicrap, Timothy, Silver, David

“…Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods…”
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Improved protein structure prediction using potentials from deep learning by Senior, Andrew W., Evans, Richard, Jumper, John, Kirkpatrick, James, Sifre, Laurent, Green, Tim, Qin, Chongli, Žídek, Augustin, Nelson, Alexander W. R., Bridgland, Alex, Penedones, Hugo, Petersen, Stig, Simonyan, Karen, Crossan, Steve, Kohli, Pushmeet, Jones, David T., Silver, David, Kavukcuoglu, Koray, Hassabis, Demis

“…Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence 1 . This problem is of fundamental…”
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Mastering the game of Go with deep neural networks and tree search by Silver, David, Huang, Aja, Maddison, Chris J., Guez, Arthur, Sifre, Laurent, van den Driessche, George, Schrittwieser, Julian, Antonoglou, Ioannis, Panneershelvam, Veda, Lanctot, Marc, Dieleman, Sander, Grewe, Dominik, Nham, John, Kalchbrenner, Nal, Sutskever, Ilya, Lillicrap, Timothy, Leach, Madeleine, Kavukcuoglu, Koray, Graepel, Thore, Hassabis, Demis

“…The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty…”
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Grandmaster level in StarCraft II using multi-agent reinforcement learning by Vinyals, Oriol, Babuschkin, Igor, Czarnecki, Wojciech M., Mathieu, Michaël, Dudzik, Andrew, Chung, Junyoung, Choi, David H., Powell, Richard, Ewalds, Timo, Georgiev, Petko, Oh, Junhyuk, Horgan, Dan, Kroiss, Manuel, Danihelka, Ivo, Huang, Aja, Sifre, Laurent, Cai, Trevor, Agapiou, John P., Jaderberg, Max, Vezhnevets, Alexander S., Leblond, Rémi, Pohlen, Tobias, Dalibard, Valentin, Budden, David, Sulsky, Yury, Molloy, James, Paine, Tom L., Gulcehre, Caglar, Wang, Ziyu, Pfaff, Tobias, Wu, Yuhuai, Ring, Roman, Yogatama, Dani, Wünsch, Dario, McKinney, Katrina, Smith, Oliver, Schaul, Tom, Lillicrap, Timothy, Kavukcuoglu, Koray, Hassabis, Demis, Apps, Chris, Silver, David

“…Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal,…”
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Mastering the game of Stratego with model-free multiagent reinforcement learning by Perolat, Julien, De Vylder, Bart, Hennes, Daniel, Tarassov, Eugene, Strub, Florian, de Boer, Vincent, Muller, Paul, Connor, Jerome T., Burch, Neil, Anthony, Thomas, McAleer, Stephen, Elie, Romuald, Cen, Sarah H., Wang, Zhe, Gruslys, Audrunas, Malysheva, Aleksandra, Khan, Mina, Ozair, Sherjil, Timbers, Finbarr, Pohlen, Toby, Eccles, Tom, Rowland, Mark, Lanctot, Marc, Lespiau, Jean-Baptiste, Piot, Bilal, Omidshafiei, Shayegan, Lockhart, Edward, Sifre, Laurent, Beauguerlange, Nathalie, Munos, Remi, Silver, David, Singh, Satinder, Hassabis, Demis, Tuyls, Karl

“…We introduce DeepNash, an autonomous agent that plays the imperfect information game Stratego at a human expert level. Stratego is one of the few iconic board…”
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Accelerating Large Language Model Decoding with Speculative Sampling by Chen, Charlie, Borgeaud, Sebastian, Irving, Geoffrey, Lespiau, Jean-Baptiste, Sifre, Laurent, Jumper, John

“…We present speculative sampling, an algorithm for accelerating transformer decoding by enabling the generation of multiple tokens from each transformer call…”
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Large-Scale Retrieval for Reinforcement Learning by Humphreys, Peter C, Guez, Arthur, Tieleman, Olivier, Sifre, Laurent, Weber, Théophane, Lillicrap, Timothy

“…Effective decision making involves flexibly relating past experiences and relevant contextual information to a novel situation. In deep reinforcement learning…”
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Self-conditioned Embedding Diffusion for Text Generation by 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…”
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Rigid-Motion Scattering for Texture Classification by SIfre, Laurent, Mallat, Stéphane

“…A rigid-motion scattering computes adaptive invariants along translations and rotations, with a deep convolutional network. Convolutions are calculated on the…”
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Muesli: Combining Improvements in Policy Optimization by Hessel, Matteo, Danihelka, Ivo, Viola, Fabio, Guez, Arthur, Schmitt, Simon, Sifre, Laurent, Weber, Theophane, Silver, David, van Hasselt, Hado

“…We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss. The update (henceforth Muesli) matches…”
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Machine Translation Decoding beyond Beam Search by Leblond, Rémi, Alayrac, Jean-Baptiste, Sifre, Laurent, Pislar, Miruna, Lespiau, Jean-Baptiste, Antonoglou, Ioannis, Simonyan, Karen, Vinyals, Oriol

“…Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only…”
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Retrieval-Augmented Reinforcement Learning by Goyal, Anirudh, Friesen, Abram L, Banino, Andrea, Weber, Theophane, Ke, Nan Rosemary, Badia, Adria Puigdomenech, Guez, Arthur, Mirza, Mehdi, Humphreys, Peter C, Konyushkova, Ksenia, Sifre, Laurent, Valko, Michal, Osindero, Simon, Lillicrap, Timothy, Heess, Nicolas, Blundell, Charles

“…Most deep reinforcement learning (RL) algorithms distill experience into parametric behavior policies or value functions via gradient updates. While effective,…”
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RecurrentGemma: Moving Past Transformers for Efficient Open Language Models by Botev, Aleksandar, De, Soham, Smith, Samuel L, Fernando, Anushan, Muraru, George-Cristian, Haroun, Ruba, Berrada, Leonard, Pascanu, Razvan, Sessa, Pier Giuseppe, Dadashi, Robert, Hussenot, Léonard, Ferret, Johan, Girgin, Sertan, Bachem, Olivier, Andreev, Alek, Kenealy, Kathleen, Mesnard, Thomas, Hardin, Cassidy, Bhupatiraju, Surya, Pathak, Shreya, Sifre, Laurent, Rivière, Morgane, Kale, Mihir Sanjay, Love, Juliette, Tafti, Pouya, Joulin, Armand, Fiedel, Noah, Senter, Evan, Chen, Yutian, Srinivasan, Srivatsan, Desjardins, Guillaume, Budden, David, Doucet, Arnaud, Vikram, Sharad, Paszke, Adam, Gale, Trevor, Borgeaud, Sebastian, Chen, Charlie, Brock, Andy, Paterson, Antonia, Brennan, Jenny, Risdal, Meg, Gundluru, Raj, Devanathan, Nesh, Mooney, Paul, Chauhan, Nilay, Culliton, Phil, Martins, Luiz Gustavo, Bandy, Elisa, Huntsperger, David, Cameron, Glenn, Zucker, Arthur, Warkentin, Tris, Peran, Ludovic, Giang, Minh, Ghahramani, Zoubin, Farabet, Clément, Kavukcuoglu, Koray, Hassabis, Demis, Hadsell, Raia, Teh, Yee Whye, de Frietas, Nando

“…We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local…”
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Training Compute-Optimal Large Language Models by Hoffmann, Jordan, Borgeaud, Sebastian, Mensch, Arthur, Buchatskaya, Elena, Cai, Trevor, Rutherford, Eliza, Casas, Diego de Las, Hendricks, Lisa Anne, Welbl, Johannes, Clark, Aidan, Hennigan, Tom, Noland, Eric, Millican, Katie, Driessche, George van den, Damoc, Bogdan, Guy, Aurelia, Osindero, Simon, Simonyan, Karen, Elsen, Erich, Rae, Jack W, Vinyals, Oriol, Sifre, Laurent

“…We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large…”
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Unified Scaling Laws for Routed Language Models by Clark, Aidan, Casas, Diego de las, Guy, Aurelia, Mensch, Arthur, Paganini, Michela, Hoffmann, Jordan, Damoc, Bogdan, Hechtman, Blake, Cai, Trevor, Borgeaud, Sebastian, Driessche, George van den, Rutherford, Eliza, Hennigan, Tom, Johnson, Matthew, Millican, Katie, Cassirer, Albin, Jones, Chris, Buchatskaya, Elena, Budden, David, Sifre, Laurent, Osindero, Simon, Vinyals, Oriol, Rae, Jack, Elsen, Erich, Kavukcuoglu, Koray, Simonyan, Karen

“…The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count. Here we study the scaling behaviors of…”
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Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning by Perolat, Julien, de Vylder, Bart, Hennes, Daniel, Tarassov, Eugene, Strub, Florian, de Boer, Vincent, Muller, Paul, Connor, Jerome T, Burch, Neil, Anthony, Thomas, McAleer, Stephen, Elie, Romuald, Cen, Sarah H, Wang, Zhe, Gruslys, Audrunas, Malysheva, Aleksandra, Khan, Mina, Ozair, Sherjil, Timbers, Finbarr, Pohlen, Toby, Eccles, Tom, Rowland, Mark, Lanctot, Marc, Lespiau, Jean-Baptiste, Piot, Bilal, Omidshafiei, Shayegan, Lockhart, Edward, Sifre, Laurent, Beauguerlange, Nathalie, Munos, Remi, Silver, David, Singh, Satinder, Hassabis, Demis, Tuyls, Karl

“…We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level…”
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