Zobrazeno 1 - 10
of 3 675
pro vyhledávání: '"Wang, XinYue"'
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks. LLMs continue to be vulnerable to external threats, particularly Denial-of-Service (DoS) attacks. Specifically, LLM-DoS attacks aim to exhaust computational r
Externí odkaz:
http://arxiv.org/abs/2412.13879
Knowledge graph (KG) technology is extensively utilized in many areas, and many companies offer applications based on KG. Nonetheless, most KG platforms necessitate expertise and tremendous time and effort from users to construct KG records manually,
Externí odkaz:
http://arxiv.org/abs/2410.08094
General intelligence requires quick adaption across tasks. While existing reinforcement learning (RL) methods have made progress in generalization, they typically assume only distribution changes between source and target domains. In this paper, we e
Externí odkaz:
http://arxiv.org/abs/2407.20651
Integrating visible and infrared images into one high-quality image, also known as visible and infrared image fusion, is a challenging yet critical task for many downstream vision tasks. Most existing works utilize pretrained deep neural networks or
Externí odkaz:
http://arxiv.org/abs/2406.19055
Publikováno v:
Managerial Finance, 2024, Vol. 50, Issue 11, pp. 1934-1953.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/MF-03-2024-0145
Publikováno v:
Matter 6, 4408-4418 (2023)
Since the initial report of the potential occurrence of room-temperature superconductivity under normal pressure [arXiv: 2307.12008], there has been significant interest in the field of condensed matter physics regarding Cu-doped Apatite (Pb10-xCux(P
Externí odkaz:
http://arxiv.org/abs/2308.05778
Convolutional neural networks (CNNs) and vision transformers (ViT) have obtained great achievements in computer vision. Recently, the research of multi-layer perceptron (MLP) architectures for vision have been popular again. Vision MLPs are designed
Externí odkaz:
http://arxiv.org/abs/2307.00592
Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early detection, personalized medicine, and clinical automation. However, the
Externí odkaz:
http://arxiv.org/abs/2306.14487
Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalization in real-world scenarios due to the vacuous generalization bound. T
Externí odkaz:
http://arxiv.org/abs/2304.14618