This deep learning-based method utilizes an RNA language model to accurately predict RNA 3D structures. This method addresses ...

In a recent article published in Scientific Reports, researchers introduced a conveyor belt surface monitoring system to ...

In a recent article, researchers explored the integration of endoscopic optical coherence tomography (OCT) with deep learning ...

@article{LI2019, title = "Deep learning in bioinformatics: Introduction, application, and perspective in the big data era", journal = "Methods", year = "2019", issn ...

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments ...

A team of researchers led by Anna-Karin Gustavsson at Rice University has developed an innovative imaging platform that ...

Binjumah, W. (2024) The Role of Machine Learning and Deep Learning Approaches to Improve Optical Communication Systems.

Wani, OA, Mahdi, SS, Yeasin, M, Kumar, SS, Gagnon, A, Danish, F, Al-Ansari, N, El-Hendawy, S and Mattar, MA (2024) Predicting ...

Abstract: Presents corrections to the paper, (Corrections to “Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada”).

Researchers have developed a deep learning-based approach that significantly streamlines the accurate identification and classification of two-dimensional (2D) materials through Raman spectroscopy. In ...

Abstract: In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models ...