Zobrazeno 1 - 10
of 3 984
pro vyhledávání: '"Vasilev, P. A."'
Autor:
Wang, Shuangqing, Sutherland, George A., Pidgeon, James P., Swainsbury, David J. K., Martin, Elizabeth C., Vasilev, Cvetelin, Hitchcock, Andrew, Gillard, Daniel J., Venkatraman, Ravi Kumar, Chekulaev, Dimitri, Tartakovskii, Alexander I., Hunter, C. Neil, Clark, Jenny
Singlet fission (SF), the spin-allowed conversion of one singlet exciton into two triplet excitons, offers a promising strategy for enhancing the efficiency of photovoltaic devices. However, realising this potential necessitates materials capable of
Externí odkaz:
http://arxiv.org/abs/2411.18801
Autor:
Khamisov, Oleg O., Vasilev, Stepan P.
In this paper virtual synchronous generation (VSG) approach is investigated in application to low- and zero-inertia grids operated by grid-forming (GFM) inverters. The key idea here is to introduce dynamic inertia and damping constants in order to ke
Externí odkaz:
http://arxiv.org/abs/2411.03998
Autor:
Arkhipkin, Vladimir, Vasilev, Viacheslav, Filatov, Andrei, Pavlov, Igor, Agafonova, Julia, Gerasimenko, Nikolai, Averchenkova, Anna, Mironova, Evelina, Bukashkin, Anton, Kulikov, Konstantin, Kuznetsov, Andrey, Dimitrov, Denis
Text-to-image (T2I) diffusion models are popular for introducing image manipulation methods, such as editing, image fusion, inpainting, etc. At the same time, image-to-video (I2V) and text-to-video (T2V) models are also built on top of T2I models. We
Externí odkaz:
http://arxiv.org/abs/2410.21061
Autor:
Vasilev, A. A., Kochkova, A. I., Polyakov, A. Y., Romanov, A. A., Matros, N. R., Alexanyan, L. A., Shchemerov, I. V., Pearton, S. J.
Publikováno v:
J. Appl. Phys. 136, 025701 (2024)
Direct observation of capture cross-section is challenging due to the need of extremely short filling pulses in the two-gate Deep-Level Transient Spectroscopy (DLTS). Simple estimation of cross-section can be done from DLTS and Admittance Spectroscop
Externí odkaz:
http://arxiv.org/abs/2410.20509
Autor:
Fletcher, Luan, van der Klis, Robert, Sedláček, Martin, Vasilev, Stefan, Athanasiadis, Christos
Publikováno v:
Transactions on Machine Learning Research 2024
The growing reproducibility crisis in machine learning has brought forward a need for careful examination of research findings. This paper investigates the claims made by Lei et al. (2023) regarding their proposed method, LICO, for enhancing post-hoc
Externí odkaz:
http://arxiv.org/abs/2410.13989
Autor:
Mercier, Thomas M., Budka, Marcin, Angele, Bernhard, Vasilev, Martin R., Slattery, Timothy J., Kirkby, Julie A
In the study of reading, eye-tracking technology offers unique insights into the time-course of how individuals extract information from text. A significant hurdle in using multi-line paragraph stimuli is the need to align eye gaze position with the
Externí odkaz:
http://arxiv.org/abs/2410.11873
Autor:
Shirokikh, Mikhail, Shenbin, Ilya, Alekseev, Anton, Volodkevich, Anna, Vasilev, Alexey, Savchenko, Andrey V., Nikolenko, Sergey
We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models. Proposed architectures include recurrent networks, Transformer-based m
Externí odkaz:
http://arxiv.org/abs/2409.20055
The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of generative transformer-based models for the Top-K sequential recommendation task, where the goal is
Externí odkaz:
http://arxiv.org/abs/2409.17730
Autor:
Samra, Abdulaziz, Frolov, Evgeney, Vasilev, Alexey, Grigorievskiy, Alexander, Vakhrushev, Anton
Data sparsity has been one of the long-standing problems for recommender systems. One of the solutions to mitigate this issue is to exploit knowledge available in other source domains. However, many cross-domain recommender systems introduce a comple
Externí odkaz:
http://arxiv.org/abs/2409.15568
Autor:
Zakharova, Anastasiia, Alexandrov, Dmitriy, Khodorchenko, Maria, Butakov, Nikolay, Vasilev, Alexey, Savchenko, Maxim, Grigorievskiy, Alexander
Machine learning (ML) models trained on datasets owned by different organizations and physically located in remote databases offer benefits in many real-world use cases. State regulations or business requirements often prevent data transfer to a cent
Externí odkaz:
http://arxiv.org/abs/2409.15558