In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Nonlinear independent component analysis (ICA) provides an appealing framework<lb>for unsupervised feature learning, but the models proposed so far are not identifiable.<lb>Here, we first propose a new intuitive principle of unsupervised deep learning<lb>from time series which uses the nonstationary structure of the data. Our learning<lb>principle, time-contrastive learning (TCL), finds a representation which allows<lb>optimal discrimination of time segments (windows). Surprisingly, we show how<lb>TCL can be related to a nonlinear ICA model, when ICA is redefined to include<lb>temporal nonstationarities. In particular, we show that TCL combined with linear<lb>ICA estimates the nonlinear ICA model up to point-wise transformations of the<lb>sources, and this solution is unique thus providing the first identifiability result<lb>for nonlinear ICA which is rigorous, constructive, as well as very general.
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA