Sparse Autoencoder

machine learningmechanistic interpretabilityneural networkssparsityunsupervised learningautoencoderfeature learningcetz

Architecture of a sparse autoencoder showing the encoding-decoding process with sparse hidden layer activations. The diagram illustrates how sparsity constraints in the hidden layer (shown in green with varying transparency indicating activation strength) lead to more efficient and interpretable feature representations. Input and output layers are shown in blue, with the sparse hidden layer demonstrating selective activation patterns that encourage the network to learn meaningful, sparse representations of the input data.