Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …

Graph neural networks (GNNs) have received remarkable success in link prediction
(GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then …

We study embedding table placement for distributed recommender systems, which aims to
partition and place the tables on multiple hardware devices (eg, GPUs) to balance the …

Reinforcement learning (RL) can assist in medical decision making using patient data
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …

The role of data in AI has been significantly magnified by the emerging concept of data-
centric AI. In contrast to the traditional model-centric paradigm, which focuses on developing …

Time series anomaly detection is a challenging task with a wide range of real-world
applications. Due to label sparsity, training a deep anomaly detector often relies on …

Virtual sample generation (VSG) technology aims to generate virtual samples based on real
samples, in order to expand the size of the datasets and improve model performance …

There is increasing interest in data-driven approaches for recommending optimal treatment
strategies in many chronic disease management and critical care applications …

Automatic and accurate classification of breast cancer in multimodal ultrasound images is
crucial to improve patients' diagnosis and treatment effect and save medical resources …