Abstract: The present disclosure relates generally to machine learning. More particularly, the present disclosure relates to systems and methods that regularize neural networks by decorrelating neurons or other parameters of the neural networks during training of the neural networks promoting these parameter to innovate over one another.

Abstract: The present disclosure relates generally to machine learning. More particularly, the present disclosure relates to systems and methods that regularize neural networks by decorrelating neurons or other parameters of the neural networks during training of the neural networks promoting these parameter to innovate over one another.

Abstract: Technologies are described herein for implementing a universal modulation coding mechanism for a data channel. A user data sequence comprising a first number of bits is received. The user data sequence is encoded into a codeword sequence comprising a second number of bits utilizing a modulation encoder. The modulation encoder may encode the user data sequence based on a set of Markov state transition probabilities. The modulation encoder may implement a data de-compressor algorithm from a lossless data compression scheme. The codeword sequence is output to the data channel. The codeword sequence is subsequently received from the data channel, and the user data sequence is decoded from the codeword sequence utilizing a modulation decoder. The modulation decoder may decode the user data sequence based on the same set of Markov states transition probabilities. The modulation de-coder may implement the data compressor algorithm from the lossless data compression scheme.