Zobrazeno 1 - 10
of 26 930
pro vyhledávání: '"A. Cremer"'
Autor:
Montero, David, Kraemer, Guido, Anghelea, Anca, Aybar, César, Brandt, Gunnar, Camps-Valls, Gustau, Cremer, Felix, Flik, Ida, Gans, Fabian, Habershon, Sarah, Ji, Chaonan, Kattenborn, Teja, Martínez-Ferrer, Laura, Martinuzzi, Francesco, Reinhardt, Martin, Söchting, Maximilian, Teber, Khalil, Mahecha, Miguel D.
Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged
Externí odkaz:
http://arxiv.org/abs/2408.02348
Autor:
Grinsztajn, Nathan, Flet-Berliac, Yannis, Azar, Mohammad Gheshlaghi, Strub, Florian, Wu, Bill, Choi, Eugene, Cremer, Chris, Ahmadian, Arash, Chandak, Yash, Pietquin, Olivier, Geist, Matthieu
To better align Large Language Models (LLMs) with human judgment, Reinforcement Learning from Human Feedback (RLHF) learns a reward model and then optimizes it using regularized RL. Recently, direct alignment methods were introduced to learn such a f
Externí odkaz:
http://arxiv.org/abs/2406.19188
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion
Autor:
Flet-Berliac, Yannis, Grinsztajn, Nathan, Strub, Florian, Choi, Eugene, Cremer, Chris, Ahmadian, Arash, Chandak, Yash, Azar, Mohammad Gheshlaghi, Pietquin, Olivier, Geist, Matthieu
Reinforcement Learning (RL) has been used to finetune Large Language Models (LLMs) using a reward model trained from preference data, to better align with human judgment. The recently introduced direct alignment methods, which are often simpler, more
Externí odkaz:
http://arxiv.org/abs/2406.19185
Autor:
Cremer, Jochen L., Kelly, Adrian, Bessa, Ricardo J., Subasic, Milos, Papadopoulos, Panagiotis N., Young, Samuel, Sagar, Amar, Marot, Antoine
Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it de
Externí odkaz:
http://arxiv.org/abs/2405.17184
Machine Learning and AI have the potential to transform data-driven scientific discovery, enabling accurate predictions for several scientific phenomena. As many scientific questions are inherently causal, this paper looks at the causal inference tas
Externí odkaz:
http://arxiv.org/abs/2405.17151
The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. Here, we propose an in-silico approach for the $\textit{de novo}$ gener
Externí odkaz:
http://arxiv.org/abs/2405.14925
Autor:
Asif, Shahidul, Chen, Hang, Cremer, Johannes, Ravan, Shantam, Tamara-Isaza, Jeyson, Lamsal, Saurabh, Ebadi, Reza, Li, Yan, Zhou, Ling-Jie, Chang, Cui-Zu, Xiao, John Q., Yacoby, Amir, Walsworth, Ronald L., Ku, Mark J. H.
The nitrogen vacancy (NV) center in diamond is an increasingly popular quantum sensor for microscopy of electrical current, magnetization, and spins. However, efficient NV-sample integration with a robust, high-quality interface remains an outstandin
Externí odkaz:
http://arxiv.org/abs/2403.10414
Autor:
Ahmadian, Arash, Cremer, Chris, Gallé, Matthias, Fadaee, Marzieh, Kreutzer, Julia, Pietquin, Olivier, Üstün, Ahmet, Hooker, Sara
AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models. Proximal Policy Optimization (PPO) has been positioned by recent literature as
Externí odkaz:
http://arxiv.org/abs/2402.14740
Autor:
Daly, Declan, DeVience, Stephen J., Huckestein, Emma, Blanchard, John W., Cremer, Johannes, Walsworth, Ronald L.
Nitrogen vacancy (NV) centers in diamond enable nuclear magnetic resonance (NMR) spectroscopy of samples at the nano- and micron scales. However, at typical tesla-scale NMR magnetic field strengths, NV-NMR protocols become difficult to implement due
Externí odkaz:
http://arxiv.org/abs/2310.08499
Accelerated development of demand response service provision by the residential sector is crucial for reducing carbon-emissions in the power sector. Along with the infrastructure advancement, encouraging the end users to participate is crucial. End u
Externí odkaz:
http://arxiv.org/abs/2310.07389