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pro vyhledávání: '"Li, Xiaodi"'
Graph Neural Networks have been widely applied in critical decision-making areas that demand interpretable predictions, leading to the flourishing development of interpretability algorithms. However, current graph interpretability algorithms tend to
Externí odkaz:
http://arxiv.org/abs/2408.17129
Predicting the response of a cancer cell line to a therapeutic drug is pivotal for personalized medicine. Despite numerous deep learning methods that have been developed for drug response prediction, integrating diverse information about biological e
Externí odkaz:
http://arxiv.org/abs/2408.12139
Recent LLM-driven visual agents mainly focus on solving image-based tasks, which limits their ability to understand dynamic scenes, making it far from real-life applications like guiding students in laboratory experiments and identifying their mistak
Externí odkaz:
http://arxiv.org/abs/2401.08392
Deep unrolling networks that utilize sparsity priors have achieved great success in dynamic magnetic resonance (MR) imaging. The convolutional neural network (CNN) is usually utilized to extract the transformed domain, and then the soft thresholding
Externí odkaz:
http://arxiv.org/abs/2307.09818
This report presents a framework called Segment And Track Anything (SAMTrack) that allows users to precisely and effectively segment and track any object in a video. Additionally, SAM-Track employs multimodal interaction methods that enable users to
Externí odkaz:
http://arxiv.org/abs/2305.06558
Autor:
Mi, Feng, Zhao, Chen, Wang, Zhuoyi, Halim, Sadaf MD, Li, Xiaodi, Wu, Zhouxiang, Khan, Latifur, Thuraisingham, Bhavani
With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such tech
Externí odkaz:
http://arxiv.org/abs/2301.08824
Two-view structure from motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM (vSLAM). Many existing end-to-end learning-based methods usually formulate it as a brute regression problem. However, the inadequate utilization of tradition
Externí odkaz:
http://arxiv.org/abs/2210.05517
Publikováno v:
In Journal of Water Process Engineering September 2024 66
Autor:
Zhao, Mengxuan, Li, Xiaodi, Yu, Jun-xia, Li, Fei, Guo, Li, Song, Guoping, Xiao, Chunqiao, Zhou, Fang, Chi, Ruan, Feng, Guoqing
Publikováno v:
In Journal of Environmental Management September 2024 367