Boltzmann machine learning with a variational quantum algorithm

A stop criterion for the Boltzmann machine learning algorithm

A Continuous Restricted Boltzmann Machine with a Hardware-Amenable Learning Algorithm

This paper proposes a continuous stochastic generative model that offers an improved ability to model analogue data, with a simple and reliable learning algorithm. The architecture forms a continuous restricted Boltzmann Machine, with a novel learning algorithm. The capabilities of the model are demonstrated with both artificial and real data.

A Parallel Algorithm of Boltzmann machine with Rejectionless Method

Parallelization is of great advantage to speed-up of calculation. In this paper, the proposition of parallelizing the rejectionless method [1] is presented. While for this parallelization the efficiency was confirmed with an experiment of TSP and the relationship with the number of cities was analyzed. Key-Word: Boltzmann machine, Hopfied network, simulated annealing, rejectionless method

A Learning Algorithm for Boltzmann Machines

The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a problem in o very short time. One kind of computation for which massively porollel networks appear to ...

A Hybrid Algorithm based on Deep Learning and Restricted Boltzmann Machine for Car Semantic Segmentation from Unmanned Aerial Vehicles (UAVs)-based Thermal Infrared Images

Nowadays, ground vehicle monitoring (GVM) is one of the areas of application in the intelligent traffic control system using image processing methods. In this context, the use of unmanned aerial vehicles based on thermal infrared (UAV-TIR) images is one of the optimal options for GVM due to the suitable spatial resolution, cost-effective and low volume of images. The methods that have been prop...