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Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks by Guttenberg, Nicholas; Biehl, Martin; Kanai, Ryota is a scholarly article available to read on EtoBox.

What is Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks about?

We present a loss function for neural networks that encompasses an idea of trivial versus non-trivial predictions, such that the network jointly determines its own prediction goals and learns to satisfy them. This permits the network to choose sub-sets of a problem which are most amenable to its abilities to focus on solving, while discarding 'distracting' elements that interfere with its learning. To do this, the network first transforms the raw data into a higher-level categorical representation, and then trains a predictor from that new time series to its future. To prevent a trivial solution of mapping the signal to zero, we introduce a measure of non-triviality via a contrast between the prediction error of the learned model with a naive model of the overall signal statistics. The transform can learn to discard uninformative and unpredictable components of the signal in favor of the features which are both highly predictive and highly predictable. This creates a coarse-grained model of the time-series dynamics, focusing on predicting the slowly varying latent parameters which control the statistics of the time-series, rather than predicting the fast details directly. The resul

Author

Guttenberg, Nicholas; Biehl, Martin; Kanai, Ryota

Published

2016

Language

EN