Zobrazeno 1 - 10
of 19 215
pro vyhledávání: '"A. Nicolaus"'
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
We present an empirical study investigating how specific properties of preference datasets, such as mixed-quality or noisy data, affect the performance of Preference Optimization (PO) algorithms. Our experiments, conducted in MuJoCo environments, rev
Externí odkaz:
http://arxiv.org/abs/2411.06568
Autor:
Vanecek, Vojtech, Paterek, Juraj, Kral, Robert, Kucerkova, Romana, Babin, Vladimir, Rohlicek, Jan, Cala, Roberto, Kratochwil, Nicolaus, Auffray, Etiennette, Nikl, Martin
Publikováno v:
Optical Materials: X 12 (2021) 100103
After the discovery of a cross-luminescence (CL) in BaF2 in 1982, a large number of CL scintillators were investigated. However, no CL scintillator superior to BaF2 has been discovered, and the research of CL scintillators has subsided. Recent techno
Externí odkaz:
http://arxiv.org/abs/2409.10823
Autor:
Opsahl, Catherine D., Jiang, Yuan, Grubb, Samantha A., Okinaka, Alan T., Chlanda, Nicolaus A., Conley, Hannah S., Kirk, Aidan D., Spielman, Sarah E., Carroll, Thomas J., Noel, Michael W.
A static electric field of a few V/cm shifts the energy levels of ultracold Rydberg atoms in a magneto-optical trap. For a given principle quantum number, most of the energy levels are nearly degenerate at zero field and fan out with increasing field
Externí odkaz:
http://arxiv.org/abs/2407.21764
Autor:
Goldie, Alexander David, Lu, Chris, Jackson, Matthew Thomas, Whiteson, Shimon, Foerster, Jakob Nicolaus
While reinforcement learning (RL) holds great potential for decision making in the real world, it suffers from a number of unique difficulties which often need specific consideration. In particular: it is highly non-stationary; suffers from high degr
Externí odkaz:
http://arxiv.org/abs/2407.07082
A nonintrusive model order reduction method for bilinear stochastic differential equations with additive noise is proposed. A reduced order model (ROM) is designed in order to approximate the statistical properties of high-dimensional systems. The dr
Externí odkaz:
http://arxiv.org/abs/2407.05724
Autor:
Gallici, Matteo, Fellows, Mattie, Ellis, Benjamin, Pou, Bartomeu, Masmitja, Ivan, Foerster, Jakob Nicolaus, Martin, Mario
Q-learning played a foundational role in the field reinforcement learning (RL). However, TD algorithms with off-policy data, such as Q-learning, or nonlinear function approximation like deep neural networks require several additional tricks to stabil
Externí odkaz:
http://arxiv.org/abs/2407.04811
Often times in imitation learning (IL), the environment we collect expert demonstrations in and the environment we want to deploy our learned policy in aren't exactly the same (e.g. demonstrations collected in simulation but deployment in the real wo
Externí odkaz:
http://arxiv.org/abs/2406.11905
Autor:
Huang, Yixing, Khodabakhshi, Zahra, Gomaa, Ahmed, Schmidt, Manuel, Fietkau, Rainer, Guckenberger, Matthias, Andratschke, Nicolaus, Bert, Christoph, Tanadini-Lang, Stephanie, Putz, Florian
Publikováno v:
Radiotherapy & Oncology. 2024, 198, 110419, 1-8
Objectives: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without for
Externí odkaz:
http://arxiv.org/abs/2405.10870
Autor:
Jackson, Matthew Thomas, Lu, Chris, Kirsch, Louis, Lange, Robert Tjarko, Whiteson, Shimon, Foerster, Jakob Nicolaus
Recent advancements in meta-learning have enabled the automatic discovery of novel reinforcement learning algorithms parameterized by surrogate objective functions. To improve upon manually designed algorithms, the parameterization of this learned ob
Externí odkaz:
http://arxiv.org/abs/2402.05828