Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

The prediction of global solar radiation for the regions is of great importance in terms of
giving directions of solar energy conversion systems (design, modeling, and operation) …

We introduce a self-supervised approach for learning node and graph level representations
by contrasting structural views of graphs. We show that unlike visual representation learning …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet,
which is 2.0% better than the state-of-the-art model that requires 3.5 B weakly labeled …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

Semi-supervised learning lately has shown much promise in improving deep learning
models when labeled data is scarce. Common among recent approaches is the use of …

Abstract Graph Convolutional Networks (GCNs) and their variants have experienced
significant attention and have become the de facto methods for learning graph …

Existing popular methods for semi-supervised learning with Graph Neural Networks (such
as the Graph Convolutional Network) provably cannot learn a general class of …

We study the problem of semi-supervised learning on graphs, for which graph neural
networks (GNNs) have been extensively explored. However, most existing GNNs inherently …