Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the
deep representations in the hyperbolic space provide high fidelity embeddings with few …

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 …

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

Learning from graph-structured data is an important task in machine learning and artificial
intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated …

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 …

Modern neural networks use building blocks such as convolutions that are equivariant to
arbitrary 2 D translations. However, these vanilla blocks are not equivariant to arbitrary 3 D …

Hyperbolic embeddings have recently gained attention in machine learning due to their
ability to represent hierarchical data more accurately and succinctly than their Euclidean …

Hyperbolic embeddings offer excellent quality with few dimensions when embedding
hierarchical data structures. We give a combinatorial construction that embeds trees into …

Computer vision tasks such as image classification, image retrieval, and few-shot learning
are currently dominated by Euclidean and spherical embeddings so that the final decisions …