The team built a DenseNet – a densely connected convolutional neural network – that learns hierarchical features directly ...
RNA is the means of translating the genetic code embedded in DNA into proteins, which serve as enzymes, transporters, ...
A team at UCSF developed a multitask deep learning framework that can effectively predict Alzheimer’s disease diagnosis, cognitive scores, and future cognitive decline using only baseline MRI and ...
Abstract: Clustering is a fundamental task in machine learning and data mining. The success of deep learning, especially deep generative models, has given birth to the next generation of clustering - ...
Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...
ABSTRACT: The rapid growth of technology impacts all aspects of modern life, including banking and financial transactions. While these industries benefit significantly from technological advancements, ...
Abstract: Deep learning has achieved outstanding success in the hyperspectral image (HSI) classification task. Almost all the current deep learning methods are used to conduct classification ...
CAD-DR is a deep learning-based system for dimensionality reduction of 3D CAD models using a 3D convolutional autoencoder. The system supports full STL to voxel transformation, encoding, ...
Sparse autoencoders (SAEs) are an unsupervised learning technique designed to decompose a neural network’s latent representations into sparse, seemingly interpretable features. While these models have ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results