This paper studies the problem of traffic flow forecasting, which aims t...
As a specific case of graph transfer learning, unsupervised domain adapt...
Demographics, Social determinants of health, and family history document...
Graph classification, aiming at learning the graph-level representations...
Graph classification is a crucial task in many real-world multimedia
app...
We present TransNormerLLM, the first linear attention-based Large Langua...
Multi-agent dynamical systems refer to scenarios where multiple units
in...
Node classification on graphs is a significant task with a wide range of...
Although graph neural networks (GNNs) have achieved impressive achieveme...
Rapid discovery of new diseases, such as COVID-19 can enable a timely
ep...
Graph neural networks have pushed state-of-the-arts in graph classificat...
In this paper, we investigate the challenge of spatio-temporal video
pre...
With the advance of large-scale model technologies, parameter-efficient
...
Sequential recommendation aims at understanding user preference by captu...
Next Point-of-Interest (POI) recommendation is a critical task in
locati...
Graph representation learning aims to effectively encode high-dimensiona...
Despite recent competitive performance across a range of vision tasks, v...
This paper studies the problem of graph-level clustering, which is a nov...
The complex driving environment brings great challenges to the visual
pe...
Extracting cause-effect entities from medical literature is an important...
Nowadays, E-commerce is increasingly integrated into our daily lives.
Me...
Predicting DNA-protein binding is an important and classic problem in
bi...
With the increasing scale and diversification of interaction behaviors i...
Hashing has been widely used in approximate nearest neighbor search for ...
Semantic relationships, such as hyponym-hypernym, cause-effect,
meronym-...
Recently, hashing is widely-used in approximate nearest neighbor search ...
Despite recent advances in the application of deep learning algorithms t...
Rumours have existed for a long time and have been known for serious
con...
Document clustering is a text mining technique used to provide better
do...