Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Vashurin, Roman"'
Autor:
Vashurin, Roman, Fadeeva, Ekaterina, Vazhentsev, Artem, Tsvigun, Akim, Vasilev, Daniil, Xing, Rui, Sadallah, Abdelrahman Boda, Rvanova, Lyudmila, Petrakov, Sergey, Panchenko, Alexander, Baldwin, Timothy, Nakov, Preslav, Panov, Maxim, Shelmanov, Artem
Uncertainty quantification (UQ) is becoming increasingly recognized as a critical component of applications that rely on machine learning (ML). The rapid proliferation of large language models (LLMs) has stimulated researchers to seek efficient and e
Externí odkaz:
http://arxiv.org/abs/2406.15627
Autor:
Fadeeva, Ekaterina, Vashurin, Roman, Tsvigun, Akim, Vazhentsev, Artem, Petrakov, Sergey, Fedyanin, Kirill, Vasilev, Daniil, Goncharova, Elizaveta, Panchenko, Alexander, Panov, Maxim, Baldwin, Timothy, Shelmanov, Artem
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields. However, a significant challenge arises as these models often "hallucinate", i.e., fabricate fac
Externí odkaz:
http://arxiv.org/abs/2311.07383
Autor:
Velikanov, Maksim, Kail, Roman, Anokhin, Ivan, Vashurin, Roman, Panov, Maxim, Zaytsev, Alexey, Yarotsky, Dmitry
A memory efficient approach to ensembling neural networks is to share most weights among the ensembled models by means of a single reference network. We refer to this strategy as Embedded Ensembling (EE); its particular examples are BatchEnsembles an
Externí odkaz:
http://arxiv.org/abs/2202.12297