The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

Image-language matching tasks have recently attracted a lot of attention in the computer
vision field. These tasks include image-sentence matching, ie, given an image query …

This paper proposes a method for learning joint embeddings of images and text using a two-
branch neural network with multiple layers of linear projections followed by nonlinearities …

In malicious URLs detection, traditional classifiers are challenged because the data volume
is huge, patterns are changing over time, and the correlations among features are …

We outline an anomaly detection method for industrial control systems (ICS) that combines
the analysis of network package contents that are transacted between ICS nodes and their …

Similarity between objects plays an important role in both human cognitive processes and
artificial systems for recognition and categorization. How to appropriately measure such …

This work provides the first unifying theoretical framework for node (positional) embeddings
and structural graph representations, bridging methods like matrix factorization and graph …

Accurate classification of different tumors in mammography plays a critical role in the early
diagnosis of breast cancer. However, owing to variations in appearance, it is a challenging …

Graph metric learning methods aim to learn the distance metric over graphs such that similar
(eg, same class) graphs are closer and dissimilar (eg, different class) graphs are farther …