Zobrazeno 1 - 10
of 14 603
pro vyhledávání: '"JU CHEN"'
For recommender systems in internet platforms, search activities provide additional insights into user interest through query-click interactions with items, and are thus widely used for enhancing personalized recommendation. However, these interacted
Externí odkaz:
http://arxiv.org/abs/2411.18631
Autor:
Ju, Chen, Wang, Haicheng, Cheng, Haozhe, Chen, Xu, Zhai, Zhonghua, Huang, Weilin, Lan, Jinsong, Xiao, Shuai, Zheng, Bo
Vision-Language Large Models (VLMs) recently become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and delay, impede potentials in the real-world scenarios. To achieve accelerat
Externí odkaz:
http://arxiv.org/abs/2407.11717
In this work, we propose Cell Variational Information Bottleneck Network (cellVIB), a convolutional neural network using information bottleneck mechanism, which can be combined with the latest feedforward network architecture in an end-to-end trainin
Externí odkaz:
http://arxiv.org/abs/2403.15082
This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way. Different from previous methods, Wear-Any-Way is a customizable solution. Besides generating high-fidelity results, our method supports users to precisely manipulate the
Externí odkaz:
http://arxiv.org/abs/2403.12965
Audio-visual segmentation (AVS) aims to segment the sounding objects in video frames. Although great progress has been witnessed, we experimentally reveal that current methods reach marginal performance gain within the use of the unlabeled frames, le
Externí odkaz:
http://arxiv.org/abs/2403.11074
Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and delay, impede potentials in real-world scenarios. To achieve acceleration for
Externí odkaz:
http://arxiv.org/abs/2312.07408
Autor:
Chen, Xu, Cheng, Zida, Yao, Jiangchao, Ju, Chen, Huang, Weilin, Lan, Jinsong, Zeng, Xiaoyi, Xiao, Shuai
Cross-domain CTR (CDCTR) prediction is an important research topic that studies how to leverage meaningful data from a related domain to help CTR prediction in target domain. Most existing CDCTR works design implicit ways to transfer knowledge across
Externí odkaz:
http://arxiv.org/abs/2312.00078
Autor:
Wei-Ju Chen, Yi-Ya Chao, Wei-Kai Huang, Wei-Fang Chang, Chii-Ruey Tzeng, Chi-Hsuan Chuang, Pei-Lun Lai, Scott C. Schuyler, Long-Yuan Li, Jean Lu
Publikováno v:
Cell Death Discovery, Vol 10, Iss 1, Pp 1-14 (2024)
Abstract The interaction between germ cells and somatic cells in the ovaries plays a crucial role in establishing the follicle reserve in mammals. Turner syndrome (TS) predominantly affects females who have a partial or complete loss of one X chromos
Externí odkaz:
https://doaj.org/article/168b2db426164ede93261e6aa1612474
Autor:
Ying-Cheng Lin, Yen-Chien Chen, Yen-Ju Chen, Hui-Min Hsieh, Yun-Yu Chen, Wen-Hong Wang, Hui-Fen Lang, Yi-Jun Liao, Yen-Chun Peng, Teng-Yu Lee, Sheng-Shun Yang, Yu-Cheng Cheng, Shao-Ciao Luo, Han-Chung Lien
Publikováno v:
BMC Nutrition, Vol 10, Iss 1, Pp 1-8 (2024)
Abstract Aim This pre-post intervention study aimed to assess the relationship between baseline dietary quality and the efficacy of a dietitian-guided weight reduction program, which has not been thoroughly documented to date. Methods Ninety-two cons
Externí odkaz:
https://doaj.org/article/7ba774dc07614b12a14ffcd8e9a523d2
Autor:
Jiun-Yih Lee, Pei-Shan Lee, Cheng-Hsien Chiang, Yi-Ping Chen, Chiung-Ju Chen, Yuan-Ming Huang, Jlan-Ren Chiu, Pei-Ching Yang, Chen-An Yeh, Jui-Ting Chang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract Underreporting of adverse events in healthcare systems is a global concern. This study aims to address the underreporting of adverse events (AE) by implementing a TRIZ-based model to identify and overcome barriers to reporting, thus filling
Externí odkaz:
https://doaj.org/article/3f59021688c04663a51c97c227c64afe