Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Graph Neural Networks (GNNs) perform well in community detection and molecule
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …

Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It
is observed that most MLTC tasks, there are dependencies or correlations among labels …

Multi-label image classification is a prediction task that aims to identify more than one label
from a given image. This paper considers the semantic consistency of the latent space …

Multi-label classification is an arduous problem given the complication in label correlation.
Whilst sharing a common goal with contrastive learning in utilizing correlations for …

In multi-label classification, the strategy of label-specific features has been shown to be
effective to learn from multi-label examples by accounting for the distinct discriminative …

The adoption of machine learning in materials science has rapidly transformed materials
property prediction. Hurdles limiting full capitalization of recent advancements in machine …

News media always pursue informing the public at large. It is impossible to overestimate the
significance of understanding the semantics of news coverage. Traditionally, a news text is …

Multi-label classification (MLC) is a prediction task where each sample can have more than
one label. We propose a novel contrastive learning boosted multi-label prediction model …