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 # Subtractive Mixture Models via Squaring: Representation and Learning
 
 This repository contains the code for reproducing the experiments of the paper
-_"Subtractive Mixture Models via Squaring: Representation and Learning"_, which has been accepted at ICLR 2024.
+[_"Subtractive Mixture Models via Squaring: Representation and Learning"_](https://openreview.net/forum?id=xIHi5nxu9P), which has been accepted at ICLR 2024.
 
 We show how to effectively represent and learn a generic class of (deep) mixture models encoding subtractions of
 probability distributions, called _squared non-monotonic PCs_ (NPC<sup>2</sup>s), and theoretically prove they can be