While neural networks can enjoy an outstanding flexibility and exhibit unprecedented performance, the mechanism behind their behavior is still not well-understood.

In each communication round, each client uniformly samples points in the neighborhood of their projected point (top-right plot), and jointly train the solution simplex.

Accurately modeling weather variation pattern from large amount of meteorological variables sequences is increasingly vital for providing efficient weather analysis support for disaster warning.

Topic models have been evolving rapidly over the years, from conventional to recent neural models.

Despite their widespread use, our understanding of deep neural networks (NNs) and their training dynamics is very much incomplete.

Scaling law that rewards large datasets, complex models and enhanced data granularity has been observed in various fields of deep learning.

Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose previously learned knowledge when acquiring new tasks.

The abundance of data has had a profound impact on machine learning (ML), both positive and negative.