Zobrazeno 1 - 10
of 447
pro vyhledávání: '"LU Mingyu"'
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 16, Iss 1, Pp 1-13 (2023)
Abstract Occupation profiling is a subtask of authorship profiling that is broadly defined as an analysis of individuals’ writing styles. Although the problem has been widely explored, no previous studies have attempted to identify Chinese classica
Externí odkaz:
https://doaj.org/article/1e28727de07143769df16deb792ff19a
Speech is usually used for constructing an automatic Alzheimer's dementia (AD) detection system, as the acoustic and linguistic abilities show a decline in people living with AD at the early stages. However, speech includes not only AD-related local
Externí odkaz:
http://arxiv.org/abs/2410.07277
As diffusion models are deployed in real-world settings, data attribution is needed to ensure fair acknowledgment for contributors of high-quality training data and to identify sources of harmful content. Previous work focuses on identifying individu
Externí odkaz:
http://arxiv.org/abs/2407.03153
Autor:
Lu Mingyu, Feng Pengfei
Publikováno v:
E3S Web of Conferences, Vol 275, p 03014 (2021)
The transportation specialty cannot be combined with reality in the construction. Traffic engineering and transportation are the combination of my country’s existing planning, and the profe ssional potential is huge. The main mission of applied uni
Externí odkaz:
https://doaj.org/article/51b868be9e4b464b9376779479a9bbc9
Publikováno v:
E3S Web of Conferences, Vol 275, p 03021 (2021)
With the implementation of the national strategy of “transportation power”, the transportation major is facing the transformation of new engineering majors. Practical education is an important link in the training of new engineering majors in app
Externí odkaz:
https://doaj.org/article/be3dddb295794b72a8180e9dddd0c6d9
Autor:
Liao, Yun, Di, Yide, Zhou, Hao, Zhu, Kaijun, Lu, Mingyu, Zhang, Yijia, Duan, Qing, Liu, Junhui
Local feature matching remains a challenging task, primarily due to difficulties in matching sparse keypoints and low-texture regions. The key to solving this problem lies in effectively and accurately integrating global and local information. To ach
Externí odkaz:
http://arxiv.org/abs/2308.15144
Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on
Externí odkaz:
http://arxiv.org/abs/2301.00557
Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival. Despite recent advances in machine learning and precision oncology, this approach remains challenging as collecting data
Externí odkaz:
http://arxiv.org/abs/2205.02944
Publikováno v:
In Journal of Alloys and Compounds 5 November 2024 1004