Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

Graph neural network (GNN) is a powerful learning approach for graph-based recommender
systems. Recently, GNNs integrated with contrastive learning have shown superior …

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via
contrastive mechanisms, is a challenging research spot. Among the recent works, hard …

Benefiting from the intrinsic supervision information exploitation capability, contrastive
learning has achieved promising performance in the field of deep graph clustering recently …

As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …

Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …

Recommender systems aim to provide personalized services to users and are playing an
increasingly important role in our daily lives. The key of recommender systems is to predict …

The existing graph neural architecture search (GNAS) methods heavily rely on supervised
labels during the search process, failing to handle ubiquitous scenarios where supervisions …