Zobrazeno 1 - 10
of 37 562
pro vyhledávání: '"Cremer"'
Publikováno v:
iForest - Biogeosciences and Forestry, Vol 16, Iss 1, Pp 243-252 (2023)
The measurement of roadside wood stacks in the forest still plays an important role in many forestry operations. Traditional manual measuring methods can be laborious, inaccurate and error-prone. Therefore, the issue is whether 2.5D or 3D optical rem
Externí odkaz:
https://doaj.org/article/a8b2ab1996df4902962877af227ec068
Autor:
Berendt Ferréol, Bajalan Iman, Wenig Charlett, Hinds Charlotte, Blaško Ľubomír, Cremer Tobias
Publikováno v:
Central European Forestry Journal, Vol 69, Iss 2, Pp 89-97 (2023)
Scots pine (Pinus sylvestris L.) is the most widely distributed pine species in the world. In Germany, as in many other European countries, it is a very important species both culturally and economically. Few studies have focused on bark volumes bein
Externí odkaz:
https://doaj.org/article/ca8d13194dbe4506b3104cb99336c043
Autor:
Lotnyk Andriy, Roddatis Vladimir, Braun Nils, Cremer Sonja, Bryja Hagen, Voss Lennart, Kienle Lorenz
Publikováno v:
BIO Web of Conferences, Vol 129, p 22040 (2024)
Externí odkaz:
https://doaj.org/article/f991890ec24b440f8aaade66de3de40a
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