Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Applying the learning mechanism of natural living beings to endow intelligent robots with
humanoid perception and decision-making wisdom becomes an important force to promote …

Synthesizing graceful and life-like behaviors for physically simulated characters has been a
fundamental challenge in computer animation. Data-driven methods that leverage motion …

Abstract Estimating and optimizing Mutual Information (MI) is core to many problems in
machine learning, but bounding MI in high dimensions is challenging. To establish tractable …

Abstract Representation learning of graph-structured data is challenging because both
graph structure and node features carry important information. Graph Neural Networks …

Unsupervised domain adaptation without accessing expensive annotation processes of
target data has achieved remarkable successes in semantic segmentation. However, most …

Abstract The Information Bottleneck (IB) provides an information theoretic principle for
representation learning, by retaining all information relevant for predicting label while …

Self-supervised learning of graph neural networks (GNN) is in great need because of the
widespread label scarcity issue in real-world graph/network data. Graph contrastive learning …

Abstract While Generative Adversarial Networks (GANs) have seen huge successes in
image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due …

Given the input graph and its label/property, several key problems of graph learning, such as
finding interpretable subgraphs, graph denoising and graph compression, can be attributed …