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 …

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many
domains of society and have recently reached the field of drug discovery. Given the …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

For most proteins annotated as enzymes, it is unknown which primary and/or secondary
reactions they catalyze. Experimental characterizations of potential substrates are time …

We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs)
for predicting the synthesizability of inorganic compounds and the selection of precursors …

The automation of all-terrain vehicles (ATVs) through the integration of advanced
technologies such as machine learning (ML) and artificial intelligence (AI) vision has …

Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …

We introduce a self-supervised learning method that focuses on beneficial properties of
representation and their abilities in generalizing to real-world tasks. The method …

Given input images, scene graph generation (SGG) aims to produce comprehensive,
graphical representations describing visual relationships among salient objects. Recently …

Fraud exists in all walks of life and detecting and preventing fraud represents an important
research question relevant to many stakeholders in society. With the rise in big data and …