This document contains supplementary materials for the paper "Learning of Discrete Graphical Models with Neural Networks". This is an adversarial experiment for NeurISE when compared to GRISE. GRISE will learn this model in the second level of its hierarchy with O(p) parameters per optimization. The neural net used here is [d=3, w=15]. The θ parameters here are chosen uniformly from [0.3,1.3].

In this paper, we provide a simple single-pass streaming algorithm for Correlation Clustering.

Consequently, recent methods have aimed to tailor the models and training procedures to stabilise the discrete updates.

The rectified linear unit (ReLU) [Fukushima, 1980, Nair and Hinton, 2010] activation has been by far the most widely used nonlinearity and successful building block in deep neural networks (DNNs).

Then dynamics ofwt is like a physical process - a satellite's motion around the earth (see illustration in Fig.1): according to