In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Although the latest high-end smartphone has powerful CPU and GPU, running deeper
convolutional neural networks (CNNs) for complex tasks such as ImageNet classification on …

In this paper, we address the problem of estimating noise level from a single image
contaminated by additive zero-mean Gaussian noise. We first provide rigorous analysis on …

A central task of Bayesian machine learning is to infer the posterior distribution of hidden
random variables given observations and calculate expectations with respect to this …

Tensor networks (TNs) and neural networks (NNs) are two fundamental data modeling
approaches. TNs were introduced to solve the curse of dimensionality in large-scale tensors …

Genetic association studies have yielded a wealth of biological discoveries. However, these
studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the …

The variational Bayesian (VB) approximation is known to be a promising approach to
Bayesian estimation, when the rigorous calculation of the Bayes posterior is intractable. The …

Low-rank tensor approximations are very promising for compression of deep neural
networks. We propose a new simple and efficient iterative approach, which alternates low …

How can we efficiently compress convolutional neural network (CNN) using depthwise
separable convolution, while retaining their accuracy on classification tasks? Depthwise …