P300 speller is a system that allows users to input letters using only electroencephalogram (EEG). A component called P300 is used to interpret the EEG in P300 speller. In order to achieve high performance in P300 speller, achieving high performance of P300 detection is essential. However, EEG waveforms are strongly dependent on the conditions of subject and/or environment, so it is not easy to...

We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learnin...

Building a good generative model for image has long been an important topic in computer vision and machine learning. Restricted Boltzmann machine (RBM) [5] is one of such models that is simple but powerful. However, its restricted form also has placed heavy constraints on the model’s representation power and scalability. Many extensions have been invented based on RBM in order to produce deeper...

In Shawe Taylor and Zerovnik introduced the Generalised Boltzmann Machine GBM as an extension to the Boltzmann machine that enables us to map constraint problems requiring more than two states onto a recurrent neural network In experiments were performed using the Mean Field Annealing approach to graph colouring using the Petford and Welsh algorithm as a GBM In this paper we extend the use of b...

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation ...

The Boltzmann equation is essential to the accurate modeling of rarefied gases. Unfortunately, traditional numerical solvers for this are too computationally expensive many practical applications. With modern interest in hypersonic flight and plasma flows, which relevant, there would be immediate value an efficient simulation method. collision integral component main contributor large complexit...

This review deals with restricted Boltzmann machine (RBM) under the light of statistical physics. The RBM is a classical family learning (ML) models which played central role in development deep learning. Viewing it as spin glass model and exhibiting various links other physics, we gather recent results dealing mean-field theory this context. First functioning can be analyzed via phase diagrams...

In higher educational institutes, many students have to struggle hard to complete different courses since there is no dedicated support offered to students who need special attention in the registered courses. Machine learning techniques can be utilized for students’ grades prediction in different courses. Such techniques would help students to improve their performance based on predicted grade...