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 …

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 …

As the size of transformer-based models continues to grow, fine-tuning these large-scale
pretrained vision models for new tasks has become increasingly parameter-intensive …

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 …

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 …

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

For image segmentation, the current standard is to perform pixel-level optimization and
inference in Euclidean output embedding spaces through linear hyperplanes. In this work …

Skeleton-based action recognition has attracted lots of research attention. Recently to build
an accurate skeleton-based action recognizer a variety of works have been proposed …