Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Shallow neural networks process the features directly, while deep networks extract features
automatically along with the training. Both models suffer from overfitting or poor …

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment.
To facilitate the screening process, we develop a deep learning system, named DeepDR …

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

The remarkable practical success of deep learning has revealed some major surprises from
a theoretical perspective. In particular, simple gradient methods easily find near-optimal …

Soft sensors are widely constructed in process industry to realize process monitoring, quality
prediction, and many other important applications. With the development of hardware and …

Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …

Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under
unprecedented and increasing pressure according to the World Health Organization (WHO) …

In this paper, we present a thorough evaluation of the efficacy of knowledge distillation and
its dependence on student and teacher architectures. Starting with the observation that more …