In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Large sparsely-activated models have obtained excellent performance in multiple domains.
However, such models are typically trained on a single modality at a time. We present the …

Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …

Multi-task learning (MTL) has been successfully applied to many recommendation
applications. However, MTL models often suffer from performance degeneration with …

Learning effective feature crosses is the key behind building recommender systems.
However, the sparse and large feature space requires exhaustive search to identify effective …

With the advent of deep learning, many dense prediction tasks, ie, tasks that produce pixel-
level predictions, have seen significant performance improvements. The typical approach is …

Sparse expert models are a thirty-year old concept re-emerging as a popular architecture in
deep learning. This class of architecture encompasses Mixture-of-Experts, Switch …

Prognostics and health management (PHM) is a critical work to ensure the reliable operation
of industrial equipment, in which health status (HS) assessment and remaining useful life …