We demonstrate that a neural network pretrained on text and fine-tuned on code solves
mathematics course problems, explains solutions, and generates questions at a human …

Creating learning models that can exhibit sophisticated reasoning abilities is one of the
greatest challenges in deep learning research, and mathematics is rapidly becoming one of …

Abstract Machine learning (ML) applications in Earth and environmental sciences (EES)
have gained incredible momentum in recent years. However, these ML applications have …

The present paper discusses a computationally efficient Deep Learning (DL) model for real-
time classification of concrete crack/non-crack and investigates the 'black-box'nature of the …

Large Language Models have not yet been broadly adapted for the analysis of scientific
datasets due in part to the unique difficulties of tokenizing numbers. We propose xVal, a …

The Science of Deep Learning emerged from courses taught by the author that have
provided thousands of students with training and experience for their academic studies, and …

There is a growing debate amongst academics and practitioners on whether interventions
made, thus far, towards Responsible AI have been enough to engage with the root causes of …

Manufacturers are constantly looking to enhance the performance of their manufacturing
systems by improving their ability to address disruptions and disturbances, while reducing …

Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and
photonics technology to overcome the constraints of conventional computing architectures …

As artificial intelligence (AI) expands its presence in healthcare, particularly within
emergency medicine (EM), there is growing urgency to explore the ethical and practical …