scholarly journals Electrocardiogram Recognization Based on Variational AutoEncoder

2018 ◽  
Author(s):  
Shaojie Chen ◽  
Zhaopeng Meng ◽  
Qing Zhao
Keyword(s):  
Variational Autoencoder

A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational Autoencoder

2021 ◽  
pp. 1-1
Author(s):  
Cunmei Ji ◽  
Yu-Tian Wang ◽  
Zhen Gao ◽  
Lei Li ◽  
Jian-Cheng Ni ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Disease Association ◽  
Learning Method ◽  
Variational Autoencoder

scholarly journals Research on Anti-noise of Image Compression Based on Variational Autoencoder

2021 ◽  
Vol 1861 (1) ◽  
pp. 012047
Author(s):  
Li Gengzeng
Keyword(s):  
Image Compression ◽  
Variational Autoencoder

scholarly journals Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder

2021 ◽  
Author(s):  
Huajian Fang ◽  
Guillaume Carbajal ◽  
Stefan Wermter ◽  
Timo Gerkmann
Keyword(s):  
Speech Enhancement ◽  
Variational Autoencoder

scholarly journals Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Osman Mamun ◽  
Madison Wenzlick ◽  
Arun Sathanur ◽  
Jeffrey Hawk ◽  
Ram Devanathan
Keyword(s):  
Pearson Correlation ◽  
Rupture Life ◽  
Model Performance ◽  
Austenitic Stainless Steels ◽  
Generative Model ◽  
Austenitic Steels ◽  
Gradient Boosting ◽  
Variational Autoencoder ◽  
Feature Importance ◽  
Boosting Algorithm

AbstractThe Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal performance. In this work, we show that the direct rupture life parameterization without intermediate LMP parameterization, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pearson correlation coefficient >0.9 for 9–12% Cr and >0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.

scholarly journals Integration of Variational Autoencoder and Spatial Clustering for Adaptive Multi-Channel Neural Speech Separation

2021 ◽  
Author(s):  
Katerina Zmolikova ◽  
Marc Delcroix ◽  
Lukas Burget ◽  
Tomohiro Nakatani ◽  
Jan Honza Cernocky
Keyword(s):  
Spatial Clustering ◽  
Speech Separation ◽  
Variational Autoencoder

Variational-autoencoder-based Environment for Interactive Sketch Tutoring Aiming for Kids

2020 ◽  
Author(s):  
Shan-Jyun Wu ◽  
Po-Shao Lin ◽  
Ping-Chia Huang ◽  
Shou-De Lin
Keyword(s):  
Variational Autoencoder

scholarly journals Chinese Character Image Completion Using a Generative Latent Variable Model

2021 ◽  
Vol 11 (2) ◽  
pp. 624
Author(s):  
In-su Jo ◽  
Dong-bin Choi ◽  
Young B. Park
Keyword(s):  
Latent Variable ◽  
Chinese Character ◽  
Image Inpainting ◽  
Latent Variable Model ◽  
Chinese Characters ◽  
Image Completion ◽  
Variable Model ◽  
Variational Autoencoder ◽  
Convolution Model ◽  
Detection Technologies

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary objects and restores corrupted images. In this paper, we propose a variational autoencoder with classification (VAE-C) model. This model is characterized by using classification areas and a class activation map (CAM). Through the classification area, the data distribution is disentangled, and then the node to be adjusted is tracked using CAM. Through the latent variable, with which the determined node value is reduced, an image from which unnecessary objects have been removed is created. The VAE-C model can be utilized not only to eliminate unnecessary objects but also to restore corrupted images. By comparing the performance of removing unnecessary objects with mask regions with convolutional neural networks (Mask R-CNN), one of the prevalent object detection technologies, and also comparing the image restoration performance with the partial convolution model (PConv) and the gated convolution model (GConv), which are image inpainting technologies, our model is proven to perform excellently in terms of removing objects and restoring corrupted areas.

Website Recommendation with Side Information Aided Variational Autoencoder

2020 ◽  
Author(s):  
Pinhao Wang ◽  
Wenzhong Li ◽  
Zepeng Yu ◽  
Baoguo Lu ◽  
Sanglu Lu
Keyword(s):  
Side Information ◽  
Variational Autoencoder
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