Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …

Inference is the task of drawing conclusions about unobserved variables given observations
of related variables. Applications range from identifying diseases from symptoms to …

Abstract Previous work [NDRM08, NDRM09] has sought the development of binary
classifiers that exploit the ability to better solve certain discrete optimization problems with …

Statistical machine learning approaches have been at the epicenter of the ongoing research
work in the field of robot learning by demonstration over the past few years. One of the most …

Ising machines are a new type of non-Neumann computer that specializes in solving
combinatorial optimization problems efficiently. The input form of Ising machines is the …

The main goal of this thesis is to develop efficient non-parametric density estimation
methods that can be integrated with deep learning architectures, for instance, convolutional …

We study the problem of distributed filtering for state space models over networks, which
aims to collaboratively estimate the states by a network of nodes. Most of existing works on …

We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM)
models based on the Chinese restaurant process (CRP). QA is a parallelized extension of …

Variational Bayes (VB) inference algorithm is used widely to estimate both the parameters
and the unobserved hidden variables in generative statistical models. The algorithm …