autoencoder
Sign in to saveAlso known as autoencoders, AE
thumb|upright=1.15|A schema of an autoencoder. An autoencoder has two main parts: an encoder that maps the message to a code, and a decoder that reconstructs the message from the code.
Research
7,707 papers- Designing molecules with autoencoder networks.Nature computational science · 2023
- Autoencoder-based myoelectric controller for prosthetic hands.Frontiers in bioengineering and biotechnology · 2023
- EEG Signal Denoising Using Beta-Variational Autoencoder.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024
- Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers.Bioinformatics (Oxford, England) · 2024
- mbSparse: an autoencoder-based imputation method to address sparsity in microbiome data.Gut microbes · 2025
via PubMed
Wikidata facts
Show 1 more fact
via Wikidata · CC0
~27 min read
Article
28 sectionsContents
- Mathematical principles
- Definition
- Training an autoencoder
- Interpretation
- Variations
- Variational autoencoder (VAE)
- Sparse autoencoder (SAE)
- Denoising autoencoder (DAE)
- Contractive autoencoder (CAE)
- Minimum description length autoencoder (MDL-AE)
- Concrete autoencoder (CAE)
- Advantages of depth
- Training
- History
- Applications
- Dimensionality reduction
- Principal component analysis
- Information retrieval
- Autoencoder hippocampus network
- Anomaly detection
- Image processing
- Drug discovery
- Popularity prediction
- Machine translation
- Communication Systems
- See also
- Further reading
- References
thumb|upright=1.15|A schema of an autoencoder. An autoencoder has two main parts: an encoder that maps the message to a code, and a decoder that reconstructs the message from the code.
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 (encoding) for a set of data, typically for dimensionality reduction, to generate lower-dimensional embeddings for subsequent use by other machine learning algorithms.