Zobrazeno 1 - 10
of 213
pro vyhledávání: '"Hoffman, Matt"'
Autor:
Gemma Team, Riviere, Morgane, Pathak, Shreya, Sessa, Pier Giuseppe, Hardin, Cassidy, Bhupatiraju, Surya, Hussenot, Léonard, Mesnard, Thomas, Shahriari, Bobak, Ramé, Alexandre, Ferret, Johan, Liu, Peter, Tafti, Pouya, Friesen, Abe, Casbon, Michelle, Ramos, Sabela, Kumar, Ravin, Lan, Charline Le, Jerome, Sammy, Tsitsulin, Anton, Vieillard, Nino, Stanczyk, Piotr, Girgin, Sertan, Momchev, Nikola, Hoffman, Matt, Thakoor, Shantanu, Grill, Jean-Bastien, Neyshabur, Behnam, Bachem, Olivier, Walton, Alanna, Severyn, Aliaksei, Parrish, Alicia, Ahmad, Aliya, Hutchison, Allen, Abdagic, Alvin, Carl, Amanda, Shen, Amy, Brock, Andy, Coenen, Andy, Laforge, Anthony, Paterson, Antonia, Bastian, Ben, Piot, Bilal, Wu, Bo, Royal, Brandon, Chen, Charlie, Kumar, Chintu, Perry, Chris, Welty, Chris, Choquette-Choo, Christopher A., Sinopalnikov, Danila, Weinberger, David, Vijaykumar, Dimple, Rogozińska, Dominika, Herbison, Dustin, Bandy, Elisa, Wang, Emma, Noland, Eric, Moreira, Erica, Senter, Evan, Eltyshev, Evgenii, Visin, Francesco, Rasskin, Gabriel, Wei, Gary, Cameron, Glenn, Martins, Gus, Hashemi, Hadi, Klimczak-Plucińska, Hanna, Batra, Harleen, Dhand, Harsh, Nardini, Ivan, Mein, Jacinda, Zhou, Jack, Svensson, James, Stanway, Jeff, Chan, Jetha, Zhou, Jin Peng, Carrasqueira, Joana, Iljazi, Joana, Becker, Jocelyn, Fernandez, Joe, van Amersfoort, Joost, Gordon, Josh, Lipschultz, Josh, Newlan, Josh, Ji, Ju-yeong, Mohamed, Kareem, Badola, Kartikeya, Black, Kat, Millican, Katie, McDonell, Keelin, Nguyen, Kelvin, Sodhia, Kiranbir, Greene, Kish, Sjoesund, Lars Lowe, Usui, Lauren, Sifre, Laurent, Heuermann, Lena, Lago, Leticia, McNealus, Lilly, Soares, Livio Baldini, Kilpatrick, Logan, Dixon, Lucas, Martins, Luciano, Reid, Machel, Singh, Manvinder, Iverson, Mark, Görner, Martin, Velloso, Mat, Wirth, Mateo, Davidow, Matt, Miller, Matt, Rahtz, Matthew, Watson, Matthew, Risdal, Meg, Kazemi, Mehran, Moynihan, Michael, Zhang, Ming, Kahng, Minsuk, Park, Minwoo, Rahman, Mofi, Khatwani, Mohit, Dao, Natalie, Bardoliwalla, Nenshad, Devanathan, Nesh, Dumai, Neta, Chauhan, Nilay, Wahltinez, Oscar, Botarda, Pankil, Barnes, Parker, Barham, Paul, Michel, Paul, Jin, Pengchong, Georgiev, Petko, Culliton, Phil, Kuppala, Pradeep, Comanescu, Ramona, Merhej, Ramona, Jana, Reena, Rokni, Reza Ardeshir, Agarwal, Rishabh, Mullins, Ryan, Saadat, Samaneh, Carthy, Sara Mc, Perrin, Sarah, Arnold, Sébastien M. R., Krause, Sebastian, Dai, Shengyang, Garg, Shruti, Sheth, Shruti, Ronstrom, Sue, Chan, Susan, Jordan, Timothy, Yu, Ting, Eccles, Tom, Hennigan, Tom, Kocisky, Tomas, Doshi, Tulsee, Jain, Vihan, Yadav, Vikas, Meshram, Vilobh, Dharmadhikari, Vishal, Barkley, Warren, Wei, Wei, Ye, Wenming, Han, Woohyun, Kwon, Woosuk, Xu, Xiang, Shen, Zhe, Gong, Zhitao, Wei, Zichuan, Cotruta, Victor, Kirk, Phoebe, Rao, Anand, Giang, Minh, Peran, Ludovic, Warkentin, Tris, Collins, Eli, Barral, Joelle, Ghahramani, Zoubin, Hadsell, Raia, Sculley, D., Banks, Jeanine, Dragan, Anca, Petrov, Slav, Vinyals, Oriol, Dean, Jeff, Hassabis, Demis, Kavukcuoglu, Koray, Farabet, Clement, Buchatskaya, Elena, Borgeaud, Sebastian, Fiedel, Noah, Joulin, Armand, Kenealy, Kathleen, Dadashi, Robert, Andreev, Alek
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the
Externí odkaz:
http://arxiv.org/abs/2408.00118
Autor:
Sessa, Pier Giuseppe, Dadashi, Robert, Hussenot, Léonard, Ferret, Johan, Vieillard, Nino, Ramé, Alexandre, Shariari, Bobak, Perrin, Sarah, Friesen, Abe, Cideron, Geoffrey, Girgin, Sertan, Stanczyk, Piotr, Michi, Andrea, Sinopalnikov, Danila, Ramos, Sabela, Héliou, Amélie, Severyn, Aliaksei, Hoffman, Matt, Momchev, Nikola, Bachem, Olivier
Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models. Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that selects the best generation am
Externí odkaz:
http://arxiv.org/abs/2407.14622
Autor:
Emedom-Nnamdi, Patrick, Friesen, Abram L., Shahriari, Bobak, de Freitas, Nando, Hoffman, Matt W.
Standard approaches to sequential decision-making exploit an agent's ability to continually interact with its environment and improve its control policy. However, due to safety, ethical, and practicality constraints, this type of trial-and-error expe
Externí odkaz:
http://arxiv.org/abs/2305.03870
Autor:
Gammage, Kim, Bennett, Erica, Bird, Matthew, Blazo, Jordan, Crozier, Alyson, Ede, Alison, Hoffman, Matt, Lee, Seungmin, Leisterer, Sascha, Locke, Sean, Pila, Eva, Stork, Matthew
Publikováno v:
Journal of Sport & Exercise Psychology; Aug2024, Vol. 46 Issue 4, p238-242, 5p
Autor:
Gulcehre, Caglar, Srinivasan, Srivatsan, Sygnowski, Jakub, Ostrovski, Georg, Farajtabar, Mehrdad, Hoffman, Matt, Pascanu, Razvan, Doucet, Arnaud
Deep neural networks are the most commonly used function approximators in offline reinforcement learning. Prior works have shown that neural nets trained with TD-learning and gradient descent can exhibit implicit regularization that can be characteri
Externí odkaz:
http://arxiv.org/abs/2207.02099
Autor:
Shahriari, Bobak, Abdolmaleki, Abbas, Byravan, Arunkumar, Friesen, Abe, Liu, Siqi, Springenberg, Jost Tobias, Heess, Nicolas, Hoffman, Matt, Riedmiller, Martin
Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks. Examples of this behavior include the D4PG and DMPO algorithms
Externí odkaz:
http://arxiv.org/abs/2204.10256
Autor:
Sountsov, Pavel, Hoffman, Matt D.
Hamiltonian Monte Carlo (HMC) is a premier Markov Chain Monte Carlo (MCMC) algorithm for continuous target distributions. Its full potential can only be unleashed when its problem-dependent hyperparameters are tuned well. The adaptation of one such h
Externí odkaz:
http://arxiv.org/abs/2110.11576
Autor:
Gulcehre, Caglar, Wang, Ziyu, Novikov, Alexander, Paine, Tom Le, Colmenarejo, Sergio Gomez, Zolna, Konrad, Agarwal, Rishabh, Merel, Josh, Mankowitz, Daniel, Paduraru, Cosmin, Dulac-Arnold, Gabriel, Li, Jerry, Norouzi, Mohammad, Hoffman, Matt, Nachum, Ofir, Tucker, George, Heess, Nicolas, de Freitas, Nando
Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to learn policies from offline datasets, thus overcoming concerns associated
Externí odkaz:
http://arxiv.org/abs/2006.13888
Gating mechanisms are widely used in neural network models, where they allow gradients to backpropagate more easily through depth or time. However, their saturation property introduces problems of its own. For example, in recurrent models these gates
Externí odkaz:
http://arxiv.org/abs/1910.09890
Publikováno v:
In Journal of Adolescent Health October 2023 73(4):625-631