Zobrazeno 1 - 10
of 18 928
pro vyhledávání: '"A, Nicolaus"'
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
Autor:
Rutherford, Alexander, Ellis, Benjamin, Gallici, Matteo, Cook, Jonathan, Lupu, Andrei, Ingvarsson, Gardar, Willi, Timon, Hammond, Ravi, Khan, Akbir, de Witt, Christian Schroeder, Souly, Alexandra, Bandyopadhyay, Saptarashmi, Samvelyan, Mikayel, Jiang, Minqi, Lange, Robert Tjarko, Whiteson, Shimon, Lacerda, Bruno, Hawes, Nick, Rocktaschel, Tim, Lu, Chris, Foerster, Jakob Nicolaus
Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally, RL environments run on the CPU, which limits their scalability with t
Externí odkaz:
http://arxiv.org/abs/2311.10090
Autor:
Useini, Vullnet, Tanadini-Lang, Stephanie, Lohmeyer, Quentin, Meboldt, Mirko, Andratschke, Nicolaus, Braun, Ralph P., García, Javier Barranco
The incidence rates of melanoma, the deadliest form of skin cancer, have been increasing steadily worldwide, presenting a significant challenge to dermatologists. Early detection of melanoma is crucial for improving patient survival rates, but identi
Externí odkaz:
http://arxiv.org/abs/2311.06691