Abstract: Recently graph auto-encoders have received increasingly widespread attention as one of the important models in the field of deep learning. Existing graph auto-encoder models only use graph ...

Abstract: Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least ...

Multiple studies have attempted to use a single type of data to predict various stages of Alzheimer’s disease (AD). However, combining multiple data modalities can improve prediction accuracy. In this ...

1 Department of Mathematics, Pan African University, Institute for Basic Sciences, Technology and Innovation (PAUISTI), Nairobi, Kenya. 2 Department of Statistics and Actuarial Science, Jomo Kenyatta ...

Interstitial lung disease (ILD) comprises diverse parenchymal lung disorders, and are an important cause of morbidity and mortality among lung diseases. Disagreement is frequently observed among ...

This project differs fundamentally from VITS, as it focuses on Singing Voice Conversion (SVC) rather than Text-to-Speech (TTS). In this project, TTS functionality is not supported, and VITS is ...

1 School of Mathematical Sciences, Guizhou Normal University, Guiyang, China. 2 School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China. With the rapid development of deep ...

Physics-informed convolutional recurrent network (PhyCRNet) can solve partial differential equations without labeled data by encoding physics constraints into the loss function. However, the ...

Yoshua Bengio, Yann LeCun, and Geoffrey Hinton are recipients of the 2018 ACM A.M. Turing Award for breakthroughs that have made deep neural networks a critical component of computing. Research on ...