Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Besse, Frederic"'
Autor:
SIMA Team, Raad, Maria Abi, Ahuja, Arun, Barros, Catarina, Besse, Frederic, Bolt, Andrew, Bolton, Adrian, Brownfield, Bethanie, Buttimore, Gavin, Cant, Max, Chakera, Sarah, Chan, Stephanie C. Y., Clune, Jeff, Collister, Adrian, Copeman, Vikki, Cullum, Alex, Dasgupta, Ishita, de Cesare, Dario, Di Trapani, Julia, Donchev, Yani, Dunleavy, Emma, Engelcke, Martin, Faulkner, Ryan, Garcia, Frankie, Gbadamosi, Charles, Gong, Zhitao, Gonzales, Lucy, Gupta, Kshitij, Gregor, Karol, Hallingstad, Arne Olav, Harley, Tim, Haves, Sam, Hill, Felix, Hirst, Ed, Hudson, Drew A., Hudson, Jony, Hughes-Fitt, Steph, Rezende, Danilo J., Jasarevic, Mimi, Kampis, Laura, Ke, Rosemary, Keck, Thomas, Kim, Junkyung, Knagg, Oscar, Kopparapu, Kavya, Lampinen, Andrew, Legg, Shane, Lerchner, Alexander, Limont, Marjorie, Liu, Yulan, Loks-Thompson, Maria, Marino, Joseph, Cussons, Kathryn Martin, Matthey, Loic, Mcloughlin, Siobhan, Mendolicchio, Piermaria, Merzic, Hamza, Mitenkova, Anna, Moufarek, Alexandre, Oliveira, Valeria, Oliveira, Yanko, Openshaw, Hannah, Pan, Renke, Pappu, Aneesh, Platonov, Alex, Purkiss, Ollie, Reichert, David, Reid, John, Richemond, Pierre Harvey, Roberts, Tyson, Ruscoe, Giles, Elias, Jaume Sanchez, Sandars, Tasha, Sawyer, Daniel P., Scholtes, Tim, Simmons, Guy, Slater, Daniel, Soyer, Hubert, Strathmann, Heiko, Stys, Peter, Tam, Allison C., Teplyashin, Denis, Terzi, Tayfun, Vercelli, Davide, Vujatovic, Bojan, Wainwright, Marcus, Wang, Jane X., Wang, Zhengdong, Wierstra, Daan, Williams, Duncan, Wong, Nathaniel, York, Sarah, Young, Nick
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires learning to ground language in perception and embodied actions, in order t
Externí odkaz:
http://arxiv.org/abs/2404.10179
Autor:
Gregor, Karol, Besse, Frederic
We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms. In this framework there is no explicit notion of an agent: instead there is an environment made of atomic elements. These elements contain neural op
Externí odkaz:
http://arxiv.org/abs/2101.07627
Autor:
Rezende, Danilo J., Danihelka, Ivo, Papamakarios, George, Ke, Nan Rosemary, Jiang, Ray, Weber, Theophane, Gregor, Karol, Merzic, Hamza, Viola, Fabio, Wang, Jane, Mitrovic, Jovana, Besse, Frederic, Antonoglou, Ioannis, Buesing, Lars
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can be computationally expensive or even intractable if the observations ar
Externí odkaz:
http://arxiv.org/abs/2002.02836
Autor:
Gregor, Karol, Rezende, Danilo Jimenez, Besse, Frederic, Wu, Yan, Merzic, Hamza, Oord, Aaron van den
When agents interact with a complex environment, they must form and maintain beliefs about the relevant aspects of that environment. We propose a way to efficiently train expressive generative models in complex environments. We show that a predictive
Externí odkaz:
http://arxiv.org/abs/1906.09237
Autor:
Buchlovsky, Peter, Budden, David, Grewe, Dominik, Jones, Chris, Aslanides, John, Besse, Frederic, Brock, Andy, Clark, Aidan, Colmenarejo, Sergio Gómez, Pope, Aedan, Viola, Fabio, Belov, Dan
We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifies writing data-parallel and model-parallel research code. The same mod
Externí odkaz:
http://arxiv.org/abs/1902.00465
Autor:
Ramalho, Tiago, Kočiský, Tomáš, Besse, Frederic, Eslami, S. M. Ali, Melis, Gábor, Viola, Fabio, Blunsom, Phil, Hermann, Karl Moritz
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.
Externí odkaz:
http://arxiv.org/abs/1807.01670
We consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels. The goal is to learn an implicit representation of the environment at a higher, more abstract leve
Externí odkaz:
http://arxiv.org/abs/1807.03149
To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which rep
Externí odkaz:
http://arxiv.org/abs/1806.03107
Autor:
Buesing, Lars, Weber, Theophane, Racaniere, Sebastien, Eslami, S. M. Ali, Rezende, Danilo, Reichert, David P., Viola, Fabio, Besse, Frederic, Gregor, Karol, Hassabis, Demis, Wierstra, Daan
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact state representations, so-calle
Externí odkaz:
http://arxiv.org/abs/1802.03006
Autor:
Fernando, Chrisantha, Banarse, Dylan, Reynolds, Malcolm, Besse, Frederic, Pfau, David, Jaderberg, Max, Lanctot, Marc, Wierstra, Daan
In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the topology of a DPPN is evolved but the weights are learned. A Lamarckian algorithm, that combines evolution
Externí odkaz:
http://arxiv.org/abs/1606.02580