Zobrazeno 1 - 10
of 1 283
pro vyhledávání: '"Shao, Lun"'
Time series forecasting remains a critical challenge across various domains, often complicated by high-dimensional data and long-term dependencies. This paper presents a novel transformer architecture for time series forecasting, incorporating two ke
Externí odkaz:
http://arxiv.org/abs/2411.01419
Autor:
Liang, Xiao, Hu, Xinyu, Zuo, Simiao, Gong, Yeyun, Lou, Qiang, Liu, Yi, Huang, Shao-Lun, Jiao, Jian
Large Language Models (LLMs) have shown superior performance in various applications and fields. To achieve better performance on specialized domains such as law and advertisement, LLMs are often continue pre-trained on in-domain data. However, exist
Externí odkaz:
http://arxiv.org/abs/2406.16694
A general reverse Pinsker's inequality is derived to give an upper bound on f-divergences in terms of total variational distance when two distributions are close measured under our proposed generalized local information geometry framework. In additio
Externí odkaz:
http://arxiv.org/abs/2406.00939
This paper studies the convex hull of $d$-dimensional samples i.i.d. generated from spherically symmetric distributions. Specifically, we derive a complete integration formula for the expected facet number of the convex hull. This formula is with res
Externí odkaz:
http://arxiv.org/abs/2402.09436
Video grounding aims to localize the target moment in an untrimmed video corresponding to a given sentence query. Existing methods typically select the best prediction from a set of predefined proposals or directly regress the target span in a single
Externí odkaz:
http://arxiv.org/abs/2310.17189
Emotion recognition in conversations (ERC) is a rapidly evolving task within the natural language processing community, which aims to detect the emotions expressed by speakers during a conversation. Recently, a growing number of ERC methods have focu
Externí odkaz:
http://arxiv.org/abs/2310.16676
Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One such approach in deep neural networks based tasks is employing a share
Externí odkaz:
http://arxiv.org/abs/2306.11867
This paper investigates the asymptotics of the maximal throughput of communication over AWGN channels by $n$ channel uses under a covert constraint in terms of an upper bound $\delta$ of Kullback-Leibler divergence (KL divergence). It is shown that t
Externí odkaz:
http://arxiv.org/abs/2305.17924
Publikováno v:
Radiation Oncology, Vol 19, Iss 1, Pp 1-14 (2024)
Abstract Background The stiffness of the tumor microenvironment (TME) directly influences cellular behaviors. Radiotherapy (RT) is a common treatment for solid tumors, but the TME can impact its efficacy. In the case of liver cancer, clinical observa
Externí odkaz:
https://doaj.org/article/859dcc3d5fce4532b2feab2fe947c091
