Zobrazeno 1 - 10
of 66 360
pro vyhledávání: '"Yongqiang An"'
Publikováno v:
NeurIPS 2024
Multi-modal contrastive learning with language supervision has presented a paradigm shift in modern machine learning. By pre-training on a web-scale dataset, multi-modal contrastive learning can learn high-quality representations that exhibit impress
Externí odkaz:
http://arxiv.org/abs/2411.02837
In this paper, channel estimation (CE) of intelligent reflecting surface aided near-field (NF) multi-user communication is investigated. Initially, the least square (LS) estimator and minimum mean square error (MMSE) estimator for the estimated chann
Externí odkaz:
http://arxiv.org/abs/2410.20992
Different from the traditional semi-supervised learning paradigm that is constrained by the close-world assumption, Generalized Category Discovery (GCD) presumes that the unlabeled dataset contains new categories not appearing in the labeled set, and
Externí odkaz:
http://arxiv.org/abs/2410.21705
This study investigates the potential of Large Language Models (LLMs) for reconstructing and constructing the physical world solely based on textual knowledge. It explores the impact of model performance on spatial understanding abilities. To enhance
Externí odkaz:
http://arxiv.org/abs/2410.17529
This work introduces a novel numerical method that relaxes the minimal configuration requirements for joint sensors and sources localization (JSSL) in 3D space using time of arrival (TOA) measurements. Traditionally, the principle requires that the n
Externí odkaz:
http://arxiv.org/abs/2410.19772
Autor:
Dou, Chengfeng, Zhang, Ying, Jin, Zhi, Jiao, Wenpin, Zhao, Haiyan, Zhao, Yongqiang, Tao, Zhengwei
This research examines the use of Reinforcement Learning from AI Feedback (RLAIF) techniques to improve healthcare dialogue models, with the aim of tackling the challenges of preference-aligned data annotation while reducing the reliance on medical e
Externí odkaz:
http://arxiv.org/abs/2410.04112
Recently, State Space Models (SSMs) with efficient hardware-aware designs, i.e., Mamba, have demonstrated significant potential in computer vision tasks due to their linear computational complexity with respect to token length and their global recept
Externí odkaz:
http://arxiv.org/abs/2410.03174
Eco-driving has been shown to reduce energy consumption for electric vehicles (EVs). Such strategies can also be implemented to both reduce energy consumption and improve battery lifetime. This study considers the eco-driving of a connected electric
Externí odkaz:
http://arxiv.org/abs/2410.01685
Autor:
Hua, Xiaoqiang, Peng, Linyu, Liu, Weijian, Cheng, Yongqiang, Wang, Hongqiang, Sun, Huafei, Wang, Zhenghua
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing 61, 5101815, 2023
This paper deals with the problem of detecting maritime targets embedded in nonhomogeneous sea clutter, where limited number of secondary data is available due to the heterogeneity of sea clutter. A class of linear discriminant analysis (LDA)-based m
Externí odkaz:
http://arxiv.org/abs/2409.17911
Autor:
Bi, Hanbo, Feng, Yingchao, Mao, Yongqiang, Pei, Jianning, Diao, Wenhui, Wang, Hongqi, Sun, Xian
Few-shot Segmentation (FSS) aims to segment the interested objects in the query image with just a handful of labeled samples (i.e., support images). Previous schemes would leverage the similarity between support-query pixel pairs to construct the pix
Externí odkaz:
http://arxiv.org/abs/2409.17453