To solve complex tasks, large language models (LLMs) often require multi...
Skin lesion segmentation is a fundamental task in dermoscopic image anal...
Rising computational demands of modern natural language processing (NLP)...
Sequential recommendation (SR) investigates the dynamic user preferences...
The unprecedented performance of large language models (LLMs) necessitat...
Event extraction (EE) is a crucial task aiming at extracting events from...
As large language models (LLMs) are continuously being developed, their
...
Finetuning pre-trained language models (LMs) enhances the models'
capabi...
We study whether multiple large language models (LLMs) can autonomously
...
Human motion generation aims to produce plausible human motion sequences...
Graph clustering is a longstanding research topic, and has achieved
rema...
State abstraction optimizes decision-making by ignoring irrelevant
envir...
Learning unbiased node representations for imbalanced samples in the gra...
Graph collaborative filtering (GCF) is a popular technique for capturing...
Role-based learning is a promising approach to improving the performance...
Graph Neural Networks (GNNs) are de facto solutions to structural data
l...
Social bot detection is of paramount importance to the resilience and
se...
To reduce the repetitive and complex work of instructors, exam paper
gen...
The Pretrained Foundation Models (PFMs) are regarded as the foundation f...
Automatic knowledge graph construction aims to manufacture structured hu...
The surprising ability of Large Language Models (LLMs) to perform well o...
Self-supervised sequential recommendation significantly improves
recomme...
Contrastive Learning (CL) has been proved to be a powerful self-supervis...
Graphs have a superior ability to represent relational data, like chemic...
Most Graph Neural Networks follow the message-passing paradigm, assuming...
Dose verification based on proton-induced positron emitters is a promisi...
Continual graph learning routinely finds its role in a variety of real-w...
The diverse relationships among real-world events, including coreference...
The attention mechanism is considered the backbone of the widely-used
Tr...
Document-level machine translation leverages inter-sentence dependencies...
Many current NLP systems are built from language models trained to optim...
We study the task of prompting large-scale language models to perform
mu...
Social networks are considered to be heterogeneous graph neural networks...
Recently, there have merged a class of task-oriented dialogue (TOD) data...
Integrating multiple online social networks (OSNs) has important implica...
Representation learning on temporal graphs has drawn considerable resear...
Topology-imbalance is a graph-specific imbalance problem caused by the u...
DBSCAN is widely used in many scientific and engineering fields because ...
Structural entropy solves the problem of measuring the amount of informa...
A challenge on Semi-Supervised and Reinforced Task-Oriented Dialog Syste...
Self-promotion of research papers on social media is ubiquitous but not
...
Graph-structured data consisting of objects (i.e., nodes) and relationsh...
The rising popularity of online social network services has attracted lo...
Temporal Expression Extraction (TEE) is essential for understanding time...
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) a...
Generative adversarial network (GAN) is widely used for generalized and
...
With the rapid development of additive manufacturing, microstructures ar...
In recent years, graph neural networks (GNNs) have emerged as a successf...
Sequential recommendation models the dynamics of a user's previous behav...
To enhance research on multimodal knowledge base and multimodal informat...