scVAE: variational auto-encoders for single-cell gene expression data
Sign Up to like & getrecommendations! Published in 2020 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btaa293
Abstract: MOTIVATION Models for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual data-processing steps, each with their own set of hyperparameter choices. With deep generative models… read more here.
Keywords: single cell; gene expression; variational auto; auto encoders ... See more keywords
Pulses Classification Based on Sparse Auto-Encoders Neural Networks
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2927724
Abstract: Unsupervised learning is applicable to classification that does not know the number of specific categories in advance, and sparse auto-encoders (SAE) are widely used for feature extraction of unsupervised learning. Therefore, this paper proposes an… read more here.
Keywords: pulses classification; sparse auto; auto encoders; classification ... See more keywords
Temporal Variational Auto-Encoders for Semi-Supervised Remaining Useful Life and Fault Diagnosis
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3174860
Abstract: Deep Learning has seen an incredible popularity surge in recent years mostly due to the state-of-the-art results obtained by neural networks. Nevertheless, within the Prognostics and Health Management community, even though its application in research… read more here.
Keywords: auto encoders; useful life; semi supervised; remaining useful ... See more keywords
HGATE: Heterogeneous Graph Attention Auto-Encoders
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2021.3138788
Abstract: Graph auto-encoder is considered a framework for unsupervised learning on graph-structured data by representing graphs in a low dimensional space. It has been proved very powerful for graph analytics. In the real world, complex relationships… read more here.
Keywords: graph attention; auto encoders; graph; heterogeneous graph ... See more keywords
Self-Supervised Variational Auto-Encoders
Sign Up to like & getrecommendations! Published in 2021 at "Entropy"
DOI: 10.3390/e23060747
Abstract: Density estimation, compression, and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of generative models, called self-supervised Variational… read more here.
Keywords: variational auto; auto encoders; self supervised; supervised variational ... See more keywords