Nonlinear ICA through low-complexity autoencoders
Sepp Hochreiter, Jürgen Schmidhuber · 2003
We train autoencoders by flat minimum search (FMS), a regularizer algorithm for finding low-complexity networks describable by few bits of information. As a by-product, this encourages nonlinear independent component analysis (ICA) and sparse codes of the input data.