Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Pan, Shirui"'
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the informati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac02bd8f0d4669fd0e2951afa0535a39
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51dd99528aa05b3bdb801ab212bfc22d
Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various grap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcbc2b9ce27a70417d5149f1bff298c3
Graph Neural Networks (GNNs) have exhibited impressive performance in many graph learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input graph data suffer from weak information, i.e., incomplete structure, incomplete feat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3f49433b6201a172480c98e6a7eb229
Text-based games (TGs) are language-based interactive environments for reinforcement learning. While language models (LMs) and knowledge graphs (KGs) are commonly used for handling large action space in TGs, it is unclear whether these techniques are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1df4baf33b93d1f74520f979b32c08c4
Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::154d301c8e0d72b7d4a1bafb7132b72f
Autor:
Zhang, Kexin, Wen, Qingsong, Zhang, Chaoli, Cai, Rongyao, Jin, Ming, Liu, Yong, Zhang, James, Liang, Yuxuan, Pang, Guansong, Song, Dongjin, Pan, Shirui
Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4af74e638f92279cfa430c83e8f0a45
Autor:
Xiong, Bo, Nayyeri, Mojtaba, Jin, Ming, He, Yunjie, Cochez, Michael, Pan, Shirui, Staab, Steffen
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions. Their prese
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0d0340ddc4d7d942c2cfd8d33d3ae89
Spectral-temporal graph neural network is a promising abstraction underlying most time series forecasting models that are based on graph neural networks (GNNs). However, more is needed to know about the underpinnings of this branch of methods. In thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01929e090af367c717952f2950177c53
Autor:
Zheng, Yu, Koh, Huan Yee, Jin, Ming, Chi, Lianhua, Phan, Khoa T., Pan, Shirui, Chen, Yi-Ping Phoebe, Xiang, Wei
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cann
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d90f1954ab289a52689225f16816600