Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Liu, Luchen"'
Autor:
Huang, Jinsheng, Chen, Liang, Guo, Taian, Zeng, Fu, Zhao, Yusheng, Wu, Bohan, Yuan, Ye, Zhao, Haozhe, Guo, Zhihui, Zhang, Yichi, Yuan, Jingyang, Ju, Wei, Liu, Luchen, Liu, Tianyu, Chang, Baobao, Zhang, Ming
Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, often assessed through multiple-choice questions (MCQs) that include an image, a question, and several options. However, many benchmarks used for suc
Externí odkaz:
http://arxiv.org/abs/2407.00468
Publikováno v:
IJCAI (2021) 1915-1921
In recent years, temporal knowledge graph (TKG) reasoning has received significant attention. Most existing methods assume that all timestamps and corresponding graphs are available during training, which makes it difficult to predict future events.
Externí odkaz:
http://arxiv.org/abs/2402.12074
Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks in recent years. However, m
Externí odkaz:
http://arxiv.org/abs/2309.04694
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution. In fact, most real-w
Externí odkaz:
http://arxiv.org/abs/2308.16609
Autor:
Luo, Xiao, Wu, Daqing, Gu, Yiyang, Chen, Chong, Liu, Luchen, Ma, Jinwen, Zhang, Ming, Deng, Minghua, Huang, Jianqiang, Hua, Xian-Sheng
Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip a
Externí odkaz:
http://arxiv.org/abs/2105.11876
Bipartite graph embedding has recently attracted much attention due to the fact that bipartite graphs are widely used in various application domains. Most previous methods, which adopt random walk-based or reconstruction-based objectives, are typical
Externí odkaz:
http://arxiv.org/abs/2012.05442
Autor:
Liu, Luchen, Liu, Zequn, Wu, Haoxian, Wang, Zichang, Shen, Jianhao, Song, Yiping, Zhang, Ming
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for directly training deep lear
Externí odkaz:
http://arxiv.org/abs/2004.05318
The advent of the Internet era has led to an explosive growth in the Electronic Health Records (EHR) in the past decades. The EHR data can be regarded as a collection of clinical events, including laboratory results, medication records, physiological
Externí odkaz:
http://arxiv.org/abs/1911.05698
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential da
Externí odkaz:
http://arxiv.org/abs/1910.06792
Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the longand irregular clini
Externí odkaz:
http://arxiv.org/abs/1903.08652