Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Ilia Kulikov"'
Publikováno v:
Journal of Magnetic Resonance Open, Vol 16, Iss , Pp 100134- (2023)
An electron spin echo in a nitroxide-containing polymer cathode film for organic radical batteries is observed for various states of charge at cryogenic temperatures. The EPR-detected state of charge (ESOC), as inferred from the number of paramagneti
Externí odkaz:
https://doaj.org/article/cf5826018c7841769fb10f75aac0a7aa
Direct speech-to-speech translation (S2ST) is among the most challenging problems in the translation paradigm due to the significant scarcity of S2ST data. While effort has been made to increase the data size from unlabeled speech by cascading pretra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dbab741318c730308c92f92f98b0cce9
http://arxiv.org/abs/2210.14514
http://arxiv.org/abs/2210.14514
Autor:
Ilia Kulikov, Naitik A. Panjwani, Anatoliy A. Vereshchagin, Domenik Spallek, Daniil A. Lukianov, Elena V. Alekseeva, Oleg V. Levin, Jan Behrends
Organic radical batteries (ORBs) are a promising class of electrochemical power sources employing organic radicals as redox-active groups. This article reports on the development of a versatile on-substrate electrode setup for spectroelectrochemical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b36918be19c20e9b6ad8ea41bd80d9e
https://refubium.fu-berlin.de/handle/fub188/35763
https://refubium.fu-berlin.de/handle/fub188/35763
Despite its wide use, recent studies have revealed unexpected and undesirable properties of neural autoregressive sequence models trained with maximum likelihood, such as an unreasonably high affinity to short sequences after training and to infinite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f75e271431c2530953d82e7a131f51a6
Publikováno v:
EMNLP (1)
Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition. We study the related issue of receiving infinite-length sequence
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f90a716e88887ab2e3f0d4a142ebec9b
http://arxiv.org/abs/2002.02492
http://arxiv.org/abs/2002.02492
Autor:
Kyunghyun Cho, Stephen Roller, Sean Welleck, Margaret Li, Ilia Kulikov, Y-Lan Boureau, Jason Weston
Publikováno v:
ACL
Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address. They tend to produce generations that (i) rely too much on copying from the context, (ii) contain repetitions within ut
Publikováno v:
INLG
We investigate the impact of search strategies in neural dialogue modeling. We first compare two standard search algorithms, greedy and beam search, as well as our newly proposed iterative beam search which produces a more diverse set of candidate re
Publikováno v:
ICASSP
It has been shown that sequence-discriminative training can improve the performance for large vocabulary continuous speech recognition. Our main contribution is a novel method for reducing the computation time of any sort of sequence training while o
Publikováno v:
ICASSP
In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network topologies on multiple GPUs. The so
Publikováno v:
Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation ISBN: 9783319582733
BDAS
BDAS
Our team is working on new algorithms for intra-page indexing in PostgreSQL generalized search trees. During this work, we encountered that slight modification of the algorithm for modification of a tuple on a page can significantly affect the perfor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9537169c3c9f53dec373547ca4f25635
https://doi.org/10.1007/978-3-319-58274-0_19
https://doi.org/10.1007/978-3-319-58274-0_19