Generative AutoEncoders require a chosen probability distribution in latent space, usually multivariate Gaussian. The original Variational AutoEncoder (VAE) uses randomness in encoder - causing problematic distortion, and overlaps in latent space for distinct inputs.
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Rise of the Machine: Detecting Aberrant Response Patterns in Survey Instruments Using Autoencoder. [PDF]
Ding C.
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A clustering method for single-cell RNA sequencing data based on denoising and masking learning. [PDF]
Xu S, Yan W, Zhang B, Qi H, Wang K.
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Transformer-based deep learning approach for obstructive sleep apnea detection using single-lead ECG. [PDF]
Almarshad MA +4 more
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Predicting oil contamination in water using machine learning on microbial compositions. [PDF]
Gao T +3 more
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Reinforced molecular dynamics: Physics-infused generative machine learning model simulates protein motion. [PDF]
Kolossváry I.
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Fast Fourier transform is a training-free, ultrafast, highly efficient, and fully interpretable approach for epigenomic data compression. [PDF]
Ward M, Dao B, Datta A, Li Z.
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Decoding the unseen: unsupervised anomaly detection in metal-organic frameworks for discovery beyond the norm. [PDF]
Alimardani H, Abaei S, Asgari M.
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A deep ensemble encoder network method for improved polygenic risk score prediction. [PDF]
Ozdemir OB, Chen R, Wu O, Li R.
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Quantum denoising autoencoder improves retinal fundus image quality for early diabetic retinopathy screening. [PDF]
Chilukuri R +3 more
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