Zobrazeno 1 - 10
of 11 991
pro vyhledávání: '"Jayaraman, P"'
Building generalist agents that can rapidly adapt to new environments is a key challenge for deploying AI in the digital and real worlds. Is scaling current agent architectures the most effective way to build generalist agents? We propose a novel app
Externí odkaz:
http://arxiv.org/abs/2412.04759
Autor:
Medi, Tejaswini, Rampini, Arianna, Reddy, Pradyumna, Jayaraman, Pradeep Kumar, Keuper, Margret
Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more efficient and controllable gen
Externí odkaz:
http://arxiv.org/abs/2411.19037
Stars in close binaries are tidally distorted, and this has a strong effect on their pulsation modes. We compute the mode frequencies and geometries of tidally distorted stars using perturbation theory, accounting for the effects of the Coriolis forc
Externí odkaz:
http://arxiv.org/abs/2411.09743
Autor:
Cho, Min Sang, Grabowski, Paul E., Thopalli, Kowshik, Jayram, Thathachar S., Barrow, Michael J., Thiagarajan, Jayaraman J., Anirudh, Rushil, Le, Hai P., Scott, Howard A., Kallman, Joshua B., Stephens, Branson C., Foord, Mark E., Gaffney, Jim A., Bremer, Peer-Timo
The integration of machine learning techniques into Inertial Confinement Fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly Non-Local Thermodynamic Equilibrium (NLTE) model with
Externí odkaz:
http://arxiv.org/abs/2411.08789
Autor:
Choi, Joanne, Clark, Sam, Jaiswal, Ranjan, Kirk, Peter, Jayaraman, Sachin, Ashqar, Huthaifa I.
Many workers in cities across the world, who have been teleworking because of the COVID-19 pandemic, are expected to be back to their commutes. As this process is believed to be gradual and telecommuting is likely to remain an option for many workers
Externí odkaz:
http://arxiv.org/abs/2411.05957
Autor:
Ma, Yecheng Jason, Hejna, Joey, Wahid, Ayzaan, Fu, Chuyuan, Shah, Dhruv, Liang, Jacky, Xu, Zhuo, Kirmani, Sean, Xu, Peng, Driess, Danny, Xiao, Ted, Tompson, Jonathan, Bastani, Osbert, Jayaraman, Dinesh, Yu, Wenhao, Zhang, Tingnan, Sadigh, Dorsa, Xia, Fei
Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal value function, across different tasks and domains requires both a la
Externí odkaz:
http://arxiv.org/abs/2411.04549
Autor:
Balakrishnan, Mayura, Bowens, Rory, Aguirre, Fernando Cruz, Hughes, Kaeli, Jayaraman, Rahul, Kuhn, Emily, Louden, Emma, Louie, Dana R., McBride, Keith, McGrath, Casey, Payne, Jacob, Presser, Tyler, Reding, Joshua S., Rickman, Emily, Scrandis, Rachel, Symons, Teresa, Wiser, Lindsey, Jahoda, Keith, Kataria, Tiffany, Nash, Alfred, X, Team
Publikováno v:
Volume 136, Number 10, 2024
We present the mission concept "Mission to Analyze the UltraViolet universE" (MAUVE), a wide-field spectrometer and imager conceived during the inaugural NASA Astrophysics Mission Design School. MAUVE responds to the 2023 Announcement of Opportunity
Externí odkaz:
http://arxiv.org/abs/2411.04164
Autor:
Liang, William, Wang, Sam, Wang, Hung-Ju, Bastani, Osbert, Jayaraman, Dinesh, Ma, Yecheng Jason
Recent work has demonstrated that a promising strategy for teaching robots a wide range of complex skills is by training them on a curriculum of progressively more challenging environments. However, developing an effective curriculum of environment d
Externí odkaz:
http://arxiv.org/abs/2411.01775
Publikováno v:
CoRL 2024
Good pre-trained visual representations could enable robots to learn visuomotor policy efficiently. Still, existing representations take a one-size-fits-all-tasks approach that comes with two important drawbacks: (1) Being completely task-agnostic, t
Externí odkaz:
http://arxiv.org/abs/2411.01284
Autor:
Hu, Edward S., Ahn, Kwangjun, Liu, Qinghua, Xu, Haoran, Tomar, Manan, Langford, Ada, Jayaraman, Dinesh, Lamb, Alex, Langford, John
We introduce the "Belief State Transformer", a next-token predictor that takes both a prefix and suffix as inputs, with a novel objective of predicting both the next token for the prefix and the previous token for the suffix. The Belief State Transfo
Externí odkaz:
http://arxiv.org/abs/2410.23506