Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Pal, Chris"'
Terrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO$_2$ emissions. The importance of predicting carbon fluxes has led to the emerging field of data-driven carbon flux modelling
Externí odkaz:
http://arxiv.org/abs/2406.04940
In order to be deployed safely, Large Language Models (LLMs) must be capable of dynamically adapting their behavior based on their level of knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-r
Externí odkaz:
http://arxiv.org/abs/2405.13022
Autor:
Rahaman, Nasim, Weiss, Martin, Wüthrich, Manuel, Bengio, Yoshua, Li, Li Erran, Pal, Chris, Schölkopf, Bernhard
This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limit access to prevent theft. To study this, we introduce an open-source
Externí odkaz:
http://arxiv.org/abs/2403.14443
Autor:
Huang, Qiuyuan, Park, Jae Sung, Gupta, Abhinav, Bennett, Paul, Gong, Ran, Som, Subhojit, Peng, Baolin, Mohammed, Owais Khan, Pal, Chris, Choi, Yejin, Gao, Jianfeng
Despite the growing adoption of mixed reality and interactive AI agents, it remains challenging for these systems to generate high quality 2D/3D scenes in unseen environments. The common practice requires deploying an AI agent to collect large amount
Externí odkaz:
http://arxiv.org/abs/2305.00970
Autor:
Rahaman, Nasim, Weiss, Martin, Träuble, Frederik, Locatello, Francesco, Lacoste, Alexandre, Bengio, Yoshua, Pal, Chris, Li, Li Erran, Schölkopf, Bernhard
Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the het
Externí odkaz:
http://arxiv.org/abs/2211.02348
Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels. It is as simple as supervised learning and Behavior Cloning (BC), bu
Externí odkaz:
http://arxiv.org/abs/2210.12272
Autor:
Rahaman, Nasim, Weiss, Martin, Locatello, Francesco, Pal, Chris, Bengio, Yoshua, Schölkopf, Bernhard, Li, Li Erran, Ballas, Nicolas
Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities. General purpose models typically make few assumptions about the underlying data-structure and are known
Externí odkaz:
http://arxiv.org/abs/2210.08031
Autor:
Hattami, Amine El, Raimondo, Stefania, Laradji, Issam, Vazquez, David, Rodriguez, Pau, Pal, Chris
Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can sometimes be codified into workflows and used to guide humans or artificial agents through the task of helping clien
Externí odkaz:
http://arxiv.org/abs/2205.11690
Recent work has shown that learned image compression strategies can outperform standard hand-crafted compression algorithms that have been developed over decades of intensive research on the rate-distortion trade-off. With growing applications of com
Externí odkaz:
http://arxiv.org/abs/2111.02249
Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL). They are designed to control how a DRL agent collects data, which is inspired by how h
Externí odkaz:
http://arxiv.org/abs/2110.03032