Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Katz, Gilad"'
Autor:
Soffair, Nitsan, Katz, Gilad
Discounted algorithms often encounter evaluation errors due to their reliance on short-term estimations, which can impede their efficacy in addressing simple, short-term tasks and impose undesired temporal discounts (\(\gamma\)). Interestingly, these
Externí odkaz:
http://arxiv.org/abs/2405.00877
Watermarking is one of the most important copyright protection tools for digital media. The most challenging type of watermarking is the imperceptible one, which embeds identifying information in the data while retaining the latter's original quality
Externí odkaz:
http://arxiv.org/abs/2305.06786
Many challenging real-world problems require the deployment of ensembles multiple complementary learning models to reach acceptable performance levels. While effective, applying the entire ensemble to every sample is costly and often unnecessary. Dee
Externí odkaz:
http://arxiv.org/abs/2209.09033
Autor:
Wang, Hao, Qu, Wenjie, Katz, Gilad, Zhu, Wenyu, Gao, Zeyu, Qiu, Han, Zhuge, Jianwei, Zhang, Chao
Binary code similarity detection (BCSD) has important applications in various fields such as vulnerability detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can comprehend inst
Externí odkaz:
http://arxiv.org/abs/2205.12713
Cloud-based machine learning services (CMLS) enable organizations to take advantage of advanced models that are pre-trained on large quantities of data. The main shortcoming of using these services, however, is the difficulty of keeping the transmitt
Externí odkaz:
http://arxiv.org/abs/2111.03125
Publikováno v:
In Information Fusion May 2024 105
In data systems, activities or events are continuously collected in the field to trace their proper executions. Logging, which means recording sequences of events, can be used for analyzing system failures and malfunctions, and identifying the causes
Externí odkaz:
http://arxiv.org/abs/2101.04053
Publikováno v:
In Expert Systems With Applications 1 March 2024 237 Part A
Multi-objective task scheduling (MOTS) is the task scheduling while optimizing multiple and possibly contradicting constraints. A challenging extension of this problem occurs when every individual task is a multi-objective optimization problem by its
Externí odkaz:
http://arxiv.org/abs/2007.09256