Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Merzić, Hamza"'
Data curation is an essential component of large-scale pretraining. In this work, we demonstrate that jointly selecting batches of data is more effective for learning than selecting examples independently. Multimodal contrastive objectives expose the
Externí odkaz:
http://arxiv.org/abs/2406.17711
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, Lawton, Rory, 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:
Evans, Talfan, Pathak, Shreya, Merzic, Hamza, Schwarz, Jonathan, Tanno, Ryutaro, Henaff, Olivier J.
Power-law scaling indicates that large-scale training with uniform sampling is prohibitively slow. Active learning methods aim to increase data efficiency by prioritizing learning on the most relevant examples. Despite their appeal, these methods hav
Externí odkaz:
http://arxiv.org/abs/2312.05328
Autor:
Ajanović, Zlatan, Aličković, Emina, Branković, Aida, Delalić, Sead, Kurtić, Eldar, Malikić, Salem, Mehonić, Adnan, Merzić, Hamza, Šehić, Kenan, Trbalić, Bahrudin
Artificial Intelligence (AI) is one of the most promising technologies of the 21. century, with an already noticeable impact on society and the economy. With this work, we provide a short overview of global trends, applications in industry and select
Externí odkaz:
http://arxiv.org/abs/2209.03990
Autor:
DeepMind Interactive Agents Team, Abramson, Josh, Ahuja, Arun, Brussee, Arthur, Carnevale, Federico, Cassin, Mary, Fischer, Felix, Georgiev, Petko, Goldin, Alex, Gupta, Mansi, Harley, Tim, Hill, Felix, Humphreys, Peter C, Hung, Alden, Landon, Jessica, Lillicrap, Timothy, Merzic, Hamza, Muldal, Alistair, Santoro, Adam, Scully, Guy, von Glehn, Tamara, Wayne, Greg, Wong, Nathaniel, Yan, Chen, Zhu, Rui
A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language. Here we study how to design artificial agents that
Externí odkaz:
http://arxiv.org/abs/2112.03763
Autor:
Hill, Felix, Tieleman, Olivier, von Glehn, Tamara, Wong, Nathaniel, Merzic, Hamza, Clark, Stephen
Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning. Here, we show that an embodied agent situated in a simulated
Externí odkaz:
http://arxiv.org/abs/2009.01719
Autor:
Das, Abhishek, Carnevale, Federico, Merzic, Hamza, Rimell, Laura, Schneider, Rosalia, Abramson, Josh, Hung, Alden, Ahuja, Arun, Clark, Stephen, Wayne, Gregory, Hill, Felix
Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments. We propose question-answering as a general paradigm to decode and understand the represe
Externí odkaz:
http://arxiv.org/abs/2006.01016
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
Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial
Externí odkaz:
http://arxiv.org/abs/1809.07004