It is well-known that exploiting label correlations is important to multi-label learning. Existing
approaches either assume that the label correlations are global and shared by all instances; …

Diffusion-weighted magnetic resonance imaging (DWI) is the only non-invasive method for
quantifying microstructure and reconstructing white-matter pathways in the living human …

With distinct privacy protection advantages, federated recommendation is becoming
increasingly feasible to store data locally in devices and federally train recommender …

In this paper, we study the problem of designing efficient convolutional neural network
architectures with the interest in eliminating the redundancy in convolution kernels. In …

When users in online social networks make a decision, they are often affected by their
neighbors. Social recommendation models utilize social information to reveal the impact of …

Recommendation from implicit feedback is a highly challenging task due to the lack of the
reliable observed negative data. A popular and effective approach for implicit …

The goal of video understanding is to develop algorithms that enable machines understand
videos at the level of human experts. Researchers have tackled various domains including …

Recommender systems typically operate on high-dimensional sparse user-item matrices.
Matrix completion is a very challenging task to predict one's interest based on millions of …

Recommender systems operate in an inherently dynamical setting. Past recommendations
influence future behavior, including which data points are observed and how user …

In data science, the ability to model the distribution of rows in tabular data and generate
realistic synthetic data enables various important applications including data compression …