Zobrazeno 1 - 10
of 14 031
pro vyhledávání: '"A. Nikulin"'
Autor:
Semkin, Valentin, Shabanov, Aleksandr, Kapralov, Kirill, Kashchenko, Mikhail, Sobolev, Alexander, Mazurenko, Ilya, Myltsev, Vladislav, Nikulin, Egor, Chernov, Alexander, Kameneva, Ekaterina, Bocharov, Alexey, Svintsov, Dmitry
Two-dimensional materials offering ultrafast photoresponse suffer from low intrinsic absorbance, especially in the mid-infrared wavelength range. Challenges in 2d material doping further complicate the creation of light-sensitive $p-n$ junctions. Her
Externí odkaz:
http://arxiv.org/abs/2411.06480
In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL). However, existing in-context RL methods, such as Algorithm Distillation (
Externí odkaz:
http://arxiv.org/abs/2411.01958
Autor:
Nikulin, Alexander, Zisman, Ilya, Zemtsov, Alexey, Sinii, Viacheslav, Kurenkov, Vladislav, Kolesnikov, Sergey
Following the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is experiencing a rapid growth. However, its development has been held back
Externí odkaz:
http://arxiv.org/abs/2406.08973
Autor:
Citrin, Jonathan, Goodfellow, Ian, Raju, Akhil, Chen, Jeremy, Degrave, Jonas, Donner, Craig, Felici, Federico, Hamel, Philippe, Huber, Andrea, Nikulin, Dmitry, Pfau, David, Tracey, Brendan, Riedmiller, Martin, Kohli, Pushmeet
We present TORAX, a new, open-source, differentiable tokamak core transport simulator implemented in Python using the JAX framework. TORAX solves the coupled equations for ion heat transport, electron heat transport, particle transport, and current d
Externí odkaz:
http://arxiv.org/abs/2406.06718
Autor:
Beckmann, Arian, Stephani, Tilman, Klotzsche, Felix, Chen, Yonghao, Hofmann, Simon M., Villringer, Arno, Gaebler, Michael, Nikulin, Vadim, Bosse, Sebastian, Eisert, Peter, Hilsmann, Anna
Since the advent of Deepfakes in digital media, the development of robust and reliable detection mechanism is urgently called for. In this study, we explore a novel approach to Deepfake detection by utilizing electroencephalography (EEG) measured fro
Externí odkaz:
http://arxiv.org/abs/2405.08527
Autor:
Huang, Shengyi, Gallouédec, Quentin, Felten, Florian, Raffin, Antonin, Dossa, Rousslan Fernand Julien, Zhao, Yanxiao, Sullivan, Ryan, Makoviychuk, Viktor, Makoviichuk, Denys, Danesh, Mohamad H., Roumégous, Cyril, Weng, Jiayi, Chen, Chufan, Rahman, Md Masudur, Araújo, João G. M., Quan, Guorui, Tan, Daniel, Klein, Timo, Charakorn, Rujikorn, Towers, Mark, Berthelot, Yann, Mehta, Kinal, Chakraborty, Dipam, KG, Arjun, Charraut, Valentin, Ye, Chang, Liu, Zichen, Alegre, Lucas N., Nikulin, Alexander, Hu, Xiao, Liu, Tianlin, Choi, Jongwook, Yi, Brent
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to repro
Externí odkaz:
http://arxiv.org/abs/2402.03046
Publikováno v:
Вавиловский журнал генетики и селекции, Vol 28, Iss 7, Pp 706-715 (2024)
During the study of algal diversity in pyroclastic deposits of the Kamchatka Peninsula, Chlorella-like green algae strains VCA-72 and VCA-93 were isolated from samples collected from along the Baydarnaya river bed on the Shiveluch volcano in 2018 and
Externí odkaz:
https://doaj.org/article/0bada90bdb694e8b98f9bdbfb4730a9f
Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined
Externí odkaz:
http://arxiv.org/abs/2312.13327
Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks. Current in-context RL methods are limited by their strict requirements for data, which n
Externí odkaz:
http://arxiv.org/abs/2312.12275
Autor:
Nikulin, Alexander, Kurenkov, Vladislav, Zisman, Ilya, Agarkov, Artem, Sinii, Viacheslav, Kolesnikov, Sergey
Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research. Written in JAX, XLand-MiniGrid is designed t
Externí odkaz:
http://arxiv.org/abs/2312.12044