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pro vyhledávání: '"Ye, Tong"'
Large language models (LLMs) have demonstrated strong capabilities in solving a wide range of programming tasks. However, LLMs have rarely been explored for code optimization. In this paper, we explore code optimization with a focus on performance en
Externí odkaz:
http://arxiv.org/abs/2406.11935
Large Language Models (LLMs) have exhibited remarkable proficiency in generating code. However, the misuse of LLM-generated (Synthetic) code has prompted concerns within both educational and industrial domains, highlighting the imperative need for th
Externí odkaz:
http://arxiv.org/abs/2405.16133
IoT devices are currently facing continuous malicious attacks due to their widespread use. Among these IoT devices, web vulnerabilities are also widely exploited because of their inherent characteristics, such as improper permission controls and inse
Externí odkaz:
http://arxiv.org/abs/2402.16043
Untrained Physics-based Deep Learning (DL) methods for digital holography have gained significant attention due to their benefits, such as not requiring an annotated training dataset, and providing interpretability since utilizing the governing laws
Externí odkaz:
http://arxiv.org/abs/2403.12056
Autor:
Ye, Tong, Wu, Lingfei, Ma, Tengfei, Zhang, Xuhong, Du, Yangkai, Liu, Peiyu, Ji, Shouling, Wang, Wenhai
Automatically generating function summaries for binaries is an extremely valuable but challenging task, since it involves translating the execution behavior and semantics of the low-level language (assembly code) into human-readable natural language.
Externí odkaz:
http://arxiv.org/abs/2310.16853
Autor:
Ye, Tong, Wu, Lingfei, Ma, Tengfei, Zhang, Xuhong, Du, Yangkai, Liu, Peiyu, Ji, Shouling, Wang, Wenhai
Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significant performance in this field, they are limited by their inability to a
Externí odkaz:
http://arxiv.org/abs/2305.11074
Deep neural retrieval models have amply demonstrated their power but estimating the reliability of their predictions remains challenging. Most dialog response retrieval models output a single score for a response on how relevant it is to a given ques
Externí odkaz:
http://arxiv.org/abs/2303.08606
Deep neural networks have achieved remarkable performance in retrieval-based dialogue systems, but they are shown to be ill calibrated. Though basic calibration methods like Monte Carlo Dropout and Ensemble can calibrate well, these methods are time-
Externí odkaz:
http://arxiv.org/abs/2303.08599
Autor:
Hong, Dannan, Ye, Tong
Ultra-reliable and low-latency communication (URLLC) is one of three major application scenarios of the 5G new radio, which has strict latency and reliability requirements. Contention-based grant-free (GF) access protocols, such as Reactive, K-Repeti
Externí odkaz:
http://arxiv.org/abs/2212.03445
Publikováno v:
Experimental Gerontology, Vol 196, Iss , Pp 112578- (2024)
Background: Heart failure (HF) is a condition caused by a malfunction of the heart's pumping function. The single-point insulin sensitivity estimator (SPISE) index is a novel indicator for assessing insulin resistance in humans. However, the connecti
Externí odkaz:
https://doaj.org/article/88c4740acd21452986231a4aa22d99d9