Deep Learning (DL) has recently enabled unprecedented advances in one of the grand
challenges in computational biology: the half-century-old problem of protein structure …

Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …

Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world in multiple levels. However …

Optimization on Riemannian manifolds-the result of smooth geometry and optimization
merging into one elegant modern framework-spans many areas of science and engineering …

Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual
concept" dog" entails all images that contain dogs. Despite being intuitive, current large …

Message-passing neural networks (MPNNs) have been successfully applied to
representation learning on graphs in a variety of real-world applications. However, two …

Knowledge graph (KG) embeddings learn low-dimensional representations of entities and
relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which …

Graph convolutional neural networks (GCNs) embed nodes in a graph into Euclidean space,
which has been shown to incur a large distortion when embedding real-world graphs with …

Metric learning aims to learn a highly discriminative model encouraging the embeddings of
similar classes to be close in the chosen metrics and pushed apart for dissimilar ones. The …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …