Given an input tensor, its CANDECOMP/PARAFAC decomposition (or CPD) is a low-rank
representation. CPDs are of particular interest in data analysis and mining, especially when …

Tensor decomposition is positioned to be a pervasive tool in the era of big data. In this
paper, we resolve many of the key algorithmic questions regarding robustness, memory …

This paper presents the optimized design and implementation of sparse tensor-times-dense
matrix multiply (SpTTM) for CPU and GPU platforms. This primitive is a critical bottleneck in …

Casting neural networks in generative frameworks is a highly sought-after endeavor these
days. Contemporary methods, such as Generative Adversarial Networks, capture some of …

Latent tree analysis seeks to model the correlations amonga set of random variables using a
tree of latent variables. It was proposed as an improvement to latent class analysis—a …

A common assumption in multiple scientific applications is that the distribution of observed
data can be modeled by a latent tree graphical model. An important example is …

This chapter offers a concise introduction to the theory of latent tree models. It highlights the
role of tree metrics in the structural description of this model class, in designing learning …

Casting neural networks in generative frameworks is a highly sought-after endeavor these
days. Existing methods, such as Generative Adversarial Networks, capture some of the …

Unsupervised learning aims at the discovery of hidden structure that drives the observations
in the real world. It is essential for success in modern machine learning and artificial …

We present an algorithm for the unsupervised learning of latent variable models based on
the method of moments. We give efficient estimates of the moments for two models that are …