Supplementary Material: Proximal Deep Structured Models

Supplementary Material - Efficient Structured Prediction with Latent Variables for General Graphical Models

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Supplementary Material of High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models

Supplementary Material: Structured Transforms for Small-Footprint Deep Learning

Proof of Proposition 1.1. The first two properties can be directly verified from the definition. The third property which will turn out to be crucial follows since applying Zf n times cycles the vector back to its original form but with all entries scaled by f . The fourth property follows becauses Zf−1 cancels the downward shift-and-scale action of Zf . The fifth property can be verified by ob...

Proximal Deep Structured Models

Many problems in real-world applications involve predicting continuous-valued random variables that are statistically related. In this paper, we propose a powerful deep structured model that is able to learn complex non-linear functions which encode the dependencies between continuous output variables. We show that inference in our model using proximal methods can be efficiently solved as a fee...

Supplementary Material: Deep Adaptive Image Clustering

This is the supplementary material for the paper entitled “Deep Adaptive Image Clustering”. The supplementary material is organized as follows. Section 1 gives the mapping function described in Figure 1. Section 2 presents the proof of Theorem 1. Section 3 details the experimental settings in our experiments. 1. The Mapping Function Utilized in Figure 1 We assume that li represents the label fe...