Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Piterbarg, Ulyana"'
Autor:
Paglieri, Davide, Cupiał, Bartłomiej, Coward, Samuel, Piterbarg, Ulyana, Wolczyk, Maciej, Khan, Akbir, Pignatelli, Eduardo, Kuciński, Łukasz, Pinto, Lerrel, Fergus, Rob, Foerster, Jakob Nicolaus, Parker-Holder, Jack, Rocktäschel, Tim
Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities; however, they still struggle to perform well in complex, dynamic environments. Real-world tasks require handling int
Externí odkaz:
http://arxiv.org/abs/2411.13543
Software engineers mainly write code by editing existing programs. In contrast, large language models (LLMs) autoregressively synthesize programs in a single pass. One explanation for this is the scarcity of open-sourced edit data. While high-quality
Externí odkaz:
http://arxiv.org/abs/2410.02749
Neural Language Models (LMs) offer an exciting solution for general-purpose embodied control. However, a key technical issue arises when using an LM-based controller: environment observations must be converted to text, which coupled with history, res
Externí odkaz:
http://arxiv.org/abs/2312.07540
Neural policy learning methods have achieved remarkable results in various control problems, ranging from Atari games to simulated locomotion. However, these methods struggle in long-horizon tasks, especially in open-ended environments with multi-mod
Externí odkaz:
http://arxiv.org/abs/2305.19240
Autor:
Ramadhan, Ali, Marshall, John, Souza, Andre, Lee, Xin Kai, Piterbarg, Ulyana, Hillier, Adeline, Wagner, Gregory LeClaire, Rackauckas, Christopher, Hill, Chris, Campin, Jean-Michel, Ferrari, Raffaele
We explore how neural differential equations (NDEs) may be trained on highly resolved fluid-dynamical models of unresolved scales providing an ideal framework for data-driven parameterizations in climate models. NDEs overcome some of the limitations
Externí odkaz:
http://arxiv.org/abs/2010.12559