Zobrazeno 1 - 10
of 37 716
pro vyhledávání: '"Just AN"'
Reinforcement learning from human feedback plays a crucial role in aligning language models towards human preferences, traditionally represented through comparisons between pairs or sets of responses within a given context. While many studies have en
Externí odkaz:
http://arxiv.org/abs/2407.14477
Static analysis is sound in theory, but an implementation may unsoundly fail to analyze all of a program's code. Any such omission is a serious threat to the validity of the tool's output. Our work is the first to measure the prevalence of these omis
Externí odkaz:
http://arxiv.org/abs/2407.07804
Building on tools that have been successfully used in the study of rational billiards, such as induced maps and interval exchange transformations, we provide a construction of a one-parameter family of isosceles triangles exhibiting non-periodic traj
Externí odkaz:
http://arxiv.org/abs/2406.17734
Machine learning is increasingly used for intrusion detection in IoT networks. This paper explores the effectiveness of using individual packet features (IPF), which are attributes extracted from a single network packet, such as timing, size, and sou
Externí odkaz:
http://arxiv.org/abs/2406.07578
Autor:
Vijayvergiya, Manushree, Salawa, Małgorzata, Budiselić, Ivan, Zheng, Dan, Lamblin, Pascal, Ivanković, Marko, Carin, Juanjo, Lewko, Mateusz, Andonov, Jovan, Petrović, Goran, Tarlow, Daniel, Maniatis, Petros, Just, René
Modern code review is a process in which an incremental code contribution made by a code author is reviewed by one or more peers before it is committed to the version control system. An important element of modern code review is verifying that code c
Externí odkaz:
http://arxiv.org/abs/2405.13565
This study investigates human and ChatGPT text simplification and its relationship to dependency distance. A set of 220 sentences, with increasing grammatical difficulty as measured in a prior user study, were simplified by a human expert and using C
Externí odkaz:
http://arxiv.org/abs/2406.17787
Digital technologies have positively transformed society, but they have also led to undesirable consequences not anticipated at the time of design or development. We posit that insights into past undesirable consequences can help researchers and prac
Externí odkaz:
http://arxiv.org/abs/2405.06783
Autor:
Kang, Feiyang, Just, Hoang Anh, Sun, Yifan, Jahagirdar, Himanshu, Zhang, Yuanzhi, Du, Rongxing, Sahu, Anit Kumar, Jia, Ruoxi
This work focuses on leveraging and selecting from vast, unlabeled, open data to pre-fine-tune a pre-trained language model. The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performan
Externí odkaz:
http://arxiv.org/abs/2405.02774
Autor:
Ishchenko, Maryna, Kovaleva, Dana A., Berczik, Peter, Kharchenko, Nina V., Piskunov, Anatoly E., Polyachenko, Evgeny, Postnikova, Ekaterina, Just, Andreas, Borodina, Olga, Omarov, Chingis, Sobodar, Olexandr
In a previous paper using Gaia DR2 data, we demonstrated that the two closely situated open clusters Collinder 135 and UBC 7 might have formed together about 50 Myr ago. In this work, we performed star-by-star dynamical modelling of the evolution of
Externí odkaz:
http://arxiv.org/abs/2404.12255
Extended dynamic mode decomposition (EDMD) is a data-driven algorithm for approximating spectral data of the Koopman operator associated to a dynamical system, combining a Galerkin method of order N and collocation method of order M. Spectral converg
Externí odkaz:
http://arxiv.org/abs/2404.08512