Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

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 …

Integrating artificial intelligence with food category recognition has been a field of interest for
research for the past few decades. It is potentially one of the next steps in revolutionizing …

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 …

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

We present a scalable approach for semi-supervised learning on graph-structured data that
is based on an efficient variant of convolutional neural networks which operate directly on …

Many interesting problems in machine learning are being revisited with new deep learning
tools. For graph-based semi-supervised learning, a recent important development is graph …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

A model must adapt itself to generalize to new and different data during testing. In this
setting of fully test-time adaptation the model has only the test data and its own parameters …

Supervised learning techniques construct predictive models by learning from a large
number of training examples, where each training example has a label indicating its ground …