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pro vyhledávání: '"Chen, Yongqiang"'
Recently there has been a surge of interest in extending the success of large language models (LLMs) to graph modality, such as social networks and molecules. As LLMs are predominantly trained with 1D text data, most existing approaches adopt a graph
Externí odkaz:
http://arxiv.org/abs/2406.14021
Interpretable graph neural networks (XGNNs ) are widely adopted in various scientific applications involving graph-structured data. Existing XGNNs predominantly adopt the attention-based mechanism to learn edge or node importance for extracting and m
Externí odkaz:
http://arxiv.org/abs/2406.07955
Large vision language models, such as CLIPs, have revolutionized modern machine learning. CLIPs have demonstrated great generalizability under distribution shifts, supported by an increasing body of literature. However, the evaluation datasets for CL
Externí odkaz:
http://arxiv.org/abs/2403.11497
Autor:
Liu, Chenxi, Chen, Yongqiang, Liu, Tongliang, Gong, Mingming, Cheng, James, Han, Bo, Zhang, Kun
Science originates with discovering new causal knowledge from a combination of known facts and observations. Traditional causal discovery approaches mainly rely on high-quality measured variables, usually given by human experts, to find causal relati
Externí odkaz:
http://arxiv.org/abs/2402.03941
Autor:
Xie, Binghui, Bian, Yatao, zhou, Kaiwen, Chen, Yongqiang, Zhao, Peilin, Han, Bo, Meng, Wei, Cheng, James
Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodologies in the field primarily concentrate on constructing models that cap
Externí odkaz:
http://arxiv.org/abs/2402.03139
Invariant graph representation learning aims to learn the invariance among data from different environments for out-of-distribution generalization on graphs. As the graph environment partitions are usually expensive to obtain, augmenting the environm
Externí odkaz:
http://arxiv.org/abs/2310.19035
Autor:
Wang, Zihao, Chen, Yongqiang, Duan, Yang, Li, Weijiang, Han, Bo, Cheng, James, Tong, Hanghang
Machine Learning (ML) techniques have found applications in estimating chemical kinetic properties. With the accumulated drug molecules identified through "AI4drug discovery", the next imperative lies in AI-driven design for high-throughput chemical
Externí odkaz:
http://arxiv.org/abs/2310.03152
Publikováno v:
口腔疾病防治, Vol 32, Iss 7, Pp 509-516 (2024)
Objective To summarize the clinicopathological characteristics and prognostic factors of salivary duct carcinoma (SDC) patients. Methods This study was reviewed and approved by the Ethics Committee, and informed consent was obtained from the patients
Externí odkaz:
https://doaj.org/article/cb454fbaa5eb4d9b97a4a15a76c9619f
A common explanation for the failure of out-of-distribution (OOD) generalization is that the model trained with empirical risk minimization (ERM) learns spurious features instead of invariant features. However, several recent studies challenged this
Externí odkaz:
http://arxiv.org/abs/2304.11327
Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks. However, much previous work covers only a few sub-types and focuses only on entity extra
Externí odkaz:
http://arxiv.org/abs/2303.18103