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Autoencoder

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation…

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Autoencoder

Nodes76
Edges75
Triples153
Avg. degree1.97
Density0.026316
Components1

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Autoencoder

Top relations

related to Further reading · 27
Autoencoder → Aaron, Adaptive, Autoencoders, Bank, Bengio, Cambridge, Cham, Courville, Data Science Handbook, Deep, Dor, Giryes, Goodfellow, Ian, ISBN, Koenigstein, Lock-gray-alt-2, Lock-green, Lock-red-alt-2, Machine Learning
related to history · 18
Autoencoder → Baldi, Boltzmann, Cottrell, Elman, Harrison, Hinton, Hornik, Immediately, In, Kramer, Munro, Oja, PCA, Salakhutdinov, Subsequently, These, This, Zipser
related to Contractive autoencoder (CAE) · 15
Autoencoder → Additionally, CAE, CAEs, DAE, DAEs, Frobenius, Gaussian, However, In, Jacobian, The, The CAE, This, Thus, To
related to Anomaly detection · 12
Autoencoder → After, Another, By, In, Intuitively, PCA, Recent, Reconstruction, Since, This, Typically, When
related to Machine translation · 7
Autoencoder → Autoencoders, Chinese, In NMT, Language-specific, Machine, NMT, Unlike
related to Minimum description length autoencoder (MDL-AE) · 7
Autoencoder → In, MDL, MDL-AE, Minimum Description Length, The, The MDL, The MDL-AE
related to Sparse autoencoder (SAE) · 7
Autoencoder → Encouraging, Inspired, One, SAE, Sparse, There, This
related to Denoising autoencoder (DAE) · 6
Autoencoder → DAE, Denoising, Kramer, Mark, That, The
related to Information retrieval · 6
Autoencoder → Autoencoders, By, Hinton, Information, Salakhutdinov, This
related to Variational autoencoder (VAE) · 6
Autoencoder → Bayesian, Despite, Given, The, VAEs, Variational

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Important terminology

autoencoders displaystyle data training learning function phi code input representation reconstruction used mathcal theta message distribution mu loss two latent

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Autoencoderis atype of artificial neural network used to learn efficient codings of unlabeled data0.90text
Autoencoderis aexpected weighted sum of sparsity losses0.90text
Autoencoderis aorthogonal projection onto this subspace0.90text
classificationinstance ofclearly separating data clusters.Reducing dimensions can improve performance on tasks0.80text
medical imaging where they have been used for image denoising as well as super-resolutioninstance ofwhere autoencoders outperformed other approaches and proved competitive against JPEG 2000.Another useful application of autoencoders in image preprocessing is image denoising.Au…0.80text
the inherent difficulty in accurately modeling the complex behavior of real-world channelsinstance ofThis approach can solve the several limitations of designing communication systems0.80text
Autoencoderhas applicationThe0.60section
Autoencoderrelated to Advantages of depthAutoencoders0.60section
Autoencoderrelated to Advantages of depthDepth0.60section
Autoencoderrelated to Advantages of depthExperimentally0.60section
Autoencoderrelated to Advantages of depthPrincipal0.60section
Autoencoderrelated to Anomaly detectionAnother0.60section

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