Zobrazeno 1 - 10
of 6 290
pro vyhledávání: '"Nikolenko AS"'
Autor:
Yang, Qi, Nikolenko, Sergey, Ongpin, Marlo, Gossoudarev, Ilia, Chu-Farseeva, Yu-Yi, Farseev, Aleksandr
Online marketing faces formidable challenges in managing and interpreting immense volumes of data necessary for competitor analysis, content research, and strategic branding. It is impossible to review hundreds to thousands of transient online conten
Externí odkaz:
http://arxiv.org/abs/2407.13117
Autor:
Pylypchuk, Oleksandr S., Ivanchenko, Serhii E., Yelisieiev, Mykola Y., Nikolenko, Andrii S., Styopkin, Victor I., Pokhylko, Bohdan, Kushnir, Vladyslav, Stetsenko, Denis O., Bereznykov, Oleksii, Leschenko, Oksana V., Eliseev, Eugene A., Poroshin, Vladimir N., Morozovsky, Nicholas V., Vainberg, Victor V., Morozovska, Anna N.
We revealed the anomalous temperature behavior of the giant dielectric permittivity and unusual frequency dependences of the pyroelectric response of the fine-grained ceramics prepared by the spark plasma sintering of the ferroelectric BaTiO3 nanopar
Externí odkaz:
http://arxiv.org/abs/2407.01108
Autor:
Gaintseva, Tatiana, Kushnareva, Laida, Magai, German, Piontkovskaya, Irina, Nikolenko, Sergey, Benning, Martin, Barannikov, Serguei, Slabaugh, Gregory
With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this problem suffer from poor generalization across domains and generative models. In t
Externí odkaz:
http://arxiv.org/abs/2406.15035
Autor:
Khrabrov, Kuzma, Ber, Anton, Tsypin, Artem, Ushenin, Konstantin, Rumiantsev, Egor, Telepov, Alexander, Protasov, Dmitry, Shenbin, Ilya, Alekseev, Anton, Shirokikh, Mikhail, Nikolenko, Sergey, Tutubalina, Elena, Kadurin, Artur
Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science. However, high computational complexity limits the scalability of their a
Externí odkaz:
http://arxiv.org/abs/2406.14347
Autor:
Shenbin, Ilya, Nikolenko, Sergey
We present ImplicitSLIM, a novel unsupervised learning approach for sparse high-dimensional data, with applications to collaborative filtering. Sparse linear methods (SLIM) and their variations show outstanding performance, but they are memory-intens
Externí odkaz:
http://arxiv.org/abs/2406.00198
Progress in neural grammatical error correction (GEC) is hindered by the lack of annotated training data. Sufficient amounts of high-quality manually annotated data are not available, so recent research has relied on generating synthetic data, pretra
Externí odkaz:
http://arxiv.org/abs/2311.11813
Autor:
Kushnareva, Laida, Gaintseva, Tatiana, Magai, German, Barannikov, Serguei, Abulkhanov, Dmitry, Kuznetsov, Kristian, Tulchinskii, Eduard, Piontkovskaya, Irina, Nikolenko, Sergey
Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated. Detecting the boundary between human-written and machine-generated parts of such
Externí odkaz:
http://arxiv.org/abs/2311.08349
Autor:
Yakovlev, Konstantin, Podolskiy, Alexander, Bout, Andrey, Nikolenko, Sergey, Piontkovskaya, Irina
Grammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-to-sequence models. However, approaches of this class are inherently slow due to one-by-one token generation, so non-autoregress
Externí odkaz:
http://arxiv.org/abs/2311.08191
Autor:
Yakovlev, Konstantin, Polyakov, Gregory, Alimova, Ilseyar, Podolskiy, Alexander, Bout, Andrey, Nikolenko, Sergey, Piontkovskaya, Irina
A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL). While this approach can bring significant improvements, it usually presumes that an entire matrix of test samples is available as DS
Externí odkaz:
http://arxiv.org/abs/2311.08143
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures. Through the patients' EEG data, we propose a meta learning framework to improve the prediction of early ict
Externí odkaz:
http://arxiv.org/abs/2310.06059