Analogy between Boltzmann Machines and Feynman Path Integrals.
Sign Up to like & getrecommendations! Published in 2023 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.3c00187
Abstract: Machine learning has had a significant impact on multiple areas of science, technology, health, and computer and information sciences. Through the advent of quantum computing, quantum machine learning has developed as a new and important… read more here.
Keywords: boltzmann machines; machine learning; machines feynman; path ... See more keywords
Restricted Boltzmann machines in quantum physics
Sign Up to like & getrecommendations! Published in 2019 at "Nature Physics"
DOI: 10.1038/s41567-019-0545-1
Abstract: A type of stochastic neural network called a restricted Boltzmann machine has been widely used in artificial intelligence applications for decades. They are now finding new life in the simulation of complex wavefunctions in quantum… read more here.
Keywords: restricted boltzmann; quantum physics; machines quantum; physics ... See more keywords
Fine-tuning restricted Boltzmann machines using quaternions and its application for spam detection
Sign Up to like & getrecommendations! Published in 2019 at "IET Networks"
DOI: 10.1049/iet-net.2018.5172
Abstract: Restricted Boltzmann Machines (RBMs) have been used in a number of applications, but only a few works have addressed them in the context of information security. However, such models have their performance severely affected by… read more here.
Keywords: restricted boltzmann; tuning restricted; harmonic search; fine tuning ... See more keywords
Identification of drug-side effect association via restricted Boltzmann machines with penalized term
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac458
Abstract: In the entire life cycle of drug development, the side effect is one of the major failure factors. Severe side effects of drugs that go undetected until the post-marketing stage leads to around two million… read more here.
Keywords: boltzmann machines; side effect; side; restricted boltzmann ... See more keywords
Learnability scaling of quantum states: Restricted Boltzmann machines
Sign Up to like & getrecommendations! Published in 2019 at "Physical Review B"
DOI: 10.1103/physrevb.100.195125
Abstract: Generative modeling with machine learning has provided a new perspective on the data-driven task of reconstructing quantum states from a set of qubit measurements. As increasingly large experimental quantum devices are built in laboratories, the… read more here.
Keywords: quantum states; number; restricted boltzmann; scaling quantum ... See more keywords
Sparse generative modeling via parameter reduction of Boltzmann machines: Application to protein-sequence families.
Sign Up to like & getrecommendations! Published in 2021 at "Physical review. E"
DOI: 10.1103/physreve.104.024407
Abstract: Boltzmann machines (BMs) are widely used as generative models. For example, pairwise Potts models (PMs), which are instances of the BM class, provide accurate statistical models of families of evolutionarily related protein sequences. Their parameters… read more here.
Keywords: parameter reduction; parameter; boltzmann machines; protein ... See more keywords
Nonequilibrium thermodynamics of restricted Boltzmann machines.
Sign Up to like & getrecommendations! Published in 2017 at "Physical Review E"
DOI: 10.1103/physreve.96.022131
Abstract: In this work, we analyze the nonequilibrium thermodynamics of a class of neural networks known as restricted Boltzmann machines (RBMs) in the context of unsupervised learning. We show how the network is described as a… read more here.
Keywords: restricted boltzmann; thermodynamics restricted; thermodynamics; unsupervised learning ... See more keywords
Purifying Deep Boltzmann Machines for Thermal Quantum States.
Sign Up to like & getrecommendations! Published in 2021 at "Physical review letters"
DOI: 10.1103/physrevlett.127.060601
Abstract: We develop two cutting-edge approaches to construct deep neural networks representing the purified finite-temperature states of quantum many-body systems. Both methods commonly aim to represent the Gibbs state by a highly expressive neural-network wave function,… read more here.
Keywords: purifying deep; deep boltzmann; machines thermal; quantum ... See more keywords
An Adaptive Deep Belief Network With Sparse Restricted Boltzmann Machines
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2019.2952864
Abstract: Deep belief network (DBN) is an efficient learning model for unknown data representation, especially nonlinear systems. However, it is extremely hard to design a satisfactory DBN with a robust structure because of traditional dense representation.… read more here.
Keywords: belief network; restricted boltzmann; sparse restricted; deep belief ... See more keywords
Restricted Boltzmann Machines as Models of Interacting Variables
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computation"
DOI: 10.1162/neco_a_01420
Abstract: Abstract We study the type of distributions that restricted Boltzmann machines (RBMs) with different activation functions can express by investigating the effect of the activation function of the hidden nodes on the marginal distribution they… read more here.
Keywords: restricted boltzmann; machines models; boltzmann machines; activation ... See more keywords
Gaussian-binary restricted Boltzmann machines for modeling natural image statistics
Sign Up to like & getrecommendations! Published in 2017 at "PLoS ONE"
DOI: 10.1371/journal.pone.0171015
Abstract: We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models. The key aspect of this analysis is to show that GRBMs can be formulated as a constrained mixture… read more here.
Keywords: restricted boltzmann; binary restricted; boltzmann machines; modeling natural ... See more keywords