IEEE

Random Walk Graph Auto-Encoders With Ensemble Networks in Graph Embedding

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 ...
GitHub

Fourier Image Transformer

Transformer architectures show spectacular performance on NLP tasks and have recently also been used for tasks such as image completion or image classification. Here we propose to use a sequential ...
IEEE

Vision-Based Fall Detection With Multi-Task Hourglass Convolutional Auto-Encoder

Abstract: Fall detection is a hot research issue in intelligent video surveillance. Falls can generate physical and psychological damage, especially for the elderly. Different from most conventional ...
scienceopen.com

Exploring the Potential of Convolutional Neural Networks in Classifying Alzheimer’s Stages with Multi-biomarker Approach

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 ...
Scientific Research Publishing

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN) ()

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 ...
GitHub

SoftVC VITS Singing Voice Conversion

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 ...
Scientific Research Publishing

End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels ()

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 ...
Europe PMC

An Efficient End-to-End Multitask Network Architecture for Defect Inspection.

Recently, computer vision-based methods have been successfully applied in many industrial fields. Nevertheless, automated detection of steel surface defects remains a challenge due to the complexity ...
Frontiers

Fourier filter-based physics- information convolutional recurrent network for 2D incompressible flow

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

Deep Learning for AI

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 ...