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Efficient learning in sparsely connected Boltzmann machines
[chapter]
1996
Lecture Notes in Computer Science
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The resulting speed-up is of immediate relevance for the speed-up of Boltzmann Machine learning rules, especially for networks with a sparse connectivity. ...
We present a heuristical procedure for efficient estimation of the partition function in the Boltzmann distribution. ...
Conclusions We have shown how the partition function of a Boltzmann Machine with a sparsely connected structure can be computed efficiently, which leads to efficient learning rules. ...
doi:10.1007/3-540-61510-5_11
fatcat:rrpsi2apqnctvd6xskjvbfvsfm
Neural network representation of tensor network and chiral states
[article]
2021
arXiv
pre-print
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These results demonstrate the power of Boltzmann machines. ...
We study the representational power of Boltzmann machines (a type of neural network) in quantum many-body systems. ...
Very recently, we became aware of some related papers [28] [29] [30] , which studied the relationship between neural and tensor network states using different methods. ...
arXiv:1701.06246v2
fatcat:mewtck6efjdsfmwwdzbd65n5f4
Boltzmann Machine and Hyperbolic Activation Function in Higher Order Network
2014
Modern Applied Science
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However, by using Boltzmann Machines and hyperbolic tangent activation function this problem can be overcome. ...
A Boltzmann machine, like a Hopfield network, is a network of units with an "energy" defined for the network. Unlike Hopfield networks, binary units of Boltzmann Machine are stochastic. ...
Thus, Boltzmann Machines had introduced to overcome this problem. A Boltzmann machine is a network of units which are fully interconnected by bidirectional connections with symmetric weights. ...
doi:10.5539/mas.v8n3p140
fatcat:2mtcsjrvsfdpnnbd5o6vpmwbvi
Neural-Network Quantum States: A Systematic Review
[article]
2022
arXiv
pre-print
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In the context of quantum many-body physics, its intersection with machine learning has configured a high-impact interdisciplinary field of study; with the arise of recent seminal contributions that have ...
Here, a systematic review of literature regarding Neural-Network Quantum States is presented. ...
In 17 , the authors review and discuss the impact of Restricted-Boltzmann machines in quantum physics, with special emphasis on NQS and condensed matter. ...
arXiv:2204.12966v1
fatcat:p6m3h5fqwnfara5fskhgyzmama
Deep Boltzmann Machines and the Centering Trick
[chapter]
2012
Lecture Notes in Computer Science
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Deep Boltzmann machines are in principle powerful models for extracting the hierarchical structure of data. ...
We test the algorithm on real data and demonstrate that our suggestion, the centered deep Boltzmann machine, learns a hierarchy of increasingly abstract representations and a better generative model of ...
Example of Boltzmann machines with different structures are shown in Figure 1 . ...
doi:10.1007/978-3-642-35289-8_33
fatcat:i3mxvfjivrhm3budikyn6vpwce
Interpretable Sparse High-Order Boltzmann Machines
2014
International Conference on Artificial Intelligence and Statistics
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Fully-observable high-order Boltzmann Machines are capable of identifying explicit highorder feature interactions theoretically. ...
To the best of our knowledge, SHBM is the first working Boltzmann Machine with explicit highorder feature interactions applied to realworld problems. ...
Section 7 concludes the paper with some discussions and future work. 2 Sparse High-order Boltzmann Machines
Review of Boltzmann Machines and High-order Boltzmann Machines In this section, we review traditional ...
dblp:conf/aistats/MinNCG14
fatcat:krx3remjybanjmgfwglichp74y
Learning thermodynamics with Boltzmann machines
2016
Physical review B
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2016 pre-print arXiv:1606.02718v1 fatcat:et6qpsxd35bsnk6nsf3p2vjiii
The trained Boltzmann machine is then used to generate spin states, for which we compare thermodynamic observables to those computed by direct MC sampling. ...
Through unsupervised learning, we train the Boltzmann machine on data sets constructed with spin configurations importance-sampled from the partition function of an Ising Hamiltonian at different temperatures ...
A natural candidate neural network for this task is a Boltzmann machine. A Boltzmann machine is a stochastic neural network, composed of neuron-like nodes forming a network with undirected edges. ...
doi:10.1103/physrevb.94.165134
fatcat:rb2ylo3drjcpzhvrvpkf6sff4e
An Overview of Hopfield Network and Boltzmann Machine
2014
International journal of computational and electronics aspects in engineering
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The two well-known and commonly used types of recurrent neural networks, Hopfield neural network and Boltzmann machine have different structures and characteristics. ...
This study gives an overview of Hopfield network and Boltzmann machine in terms of architectures, learning algorithms, comparison between these two networks from several different aspects as well as their ...
A Boltzmann machine, like a Hopfield
network, is a network of units with an "energy" defined for
the network. ...
doi:10.26706/ijceae.1.1.20141205
fatcat:ost6kvnxzjcqrdi4qa6oww3ybi
Deep Restricted Boltzmann Networks
[article]
2016
arXiv
pre-print
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Restricted Boltzmann machine (RBM) is one of such models that is simple but powerful. ...
The most famous ones among them are deep belief network, which stacks multiple layer-wise pretrained RBMs to form a hybrid model, and deep Boltzmann machine, which allows connections between hidden units ...
a deep Boltzmann machine. ...
arXiv:1611.07917v1
fatcat:ovfy5n7lzbfw3mpesop5ulgvym
Connectionist learning of belief networks
1992
Artificial Intelligence
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Experimental results show that, as a result, learning in a sigmoid belief network can be faster than in a Boltzmann machine. ...
Due to the directed nature of the connections in a belief network, however, the "negative phase" of Boltzmann machine learning is unnecessary. ...
This paper begins with reviews of Boltzmann machines and belief networks. ...
doi:10.1016/0004-3702(92)90065-6
fatcat:qkr3vdvj7zgoblkqflw2mzwyhq
Linear Neurons and Their Learning Algorithms
2018
Journal of Computer Science and Information Technology
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First, we briefly review the Boltzmann Machine and the fact that the invariant distributions of the Boltzmann Machine generate Markov chains. ...
The building block of a DBN is a probabilistic model called a Restricted Boltzmann machine (RBM), used to represent one layer of the model. ...
Each configuration of a Boltzmann machine is a state of the Markov chain. A Boltzmann machine has a stable distribution. Let T be the parameter space of a family of Boltzmann machines. ...
doi:10.15640/jcsit.v6n2a1
fatcat:gdi427au6fhoxhwglztzo7jpxa
Boltzmann Machine and Hyperbolic activation function in Higher Order Neuro Symbolic Integration
2015
International journal of computational and electronics aspects in engineering
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Index terms -Boltzmann machine, agent based modelling and hyperbolic tangent activation function I. ...
Hereby we employed Boltzmann Machines and hyperbolic tangent activation function to increased the performance of neuro symbolic integration. We used agent based modelling to model this problem. ...
A Boltzmann machine is made up of stochastic units. ...
doi:10.26706/ijceae.1.1.20150103
fatcat:6piw6wvbjbapjji2g7rpu4yc7a
Securing 5G-IoT Device Connectivity and Coverage Using Boltzmann Machine Keys Generation
2021
Mathematical Problems in Engineering
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In this research, we proposed a Boltzmann machine (BMKG)-based encryption algorithm for securing 5G-enabled IoT device network environment and compared various asymmetric algorithms for key exchange. ...
Because purposeful faults can quickly render the entire network dysfunctional, they are more difficult to identify than unexpected failures. ...
for generating other machine states. ...
doi:10.1155/2021/2330049
fatcat:zhebkqqixfapzlmzi3gkupyxju
On the challenges of using D-Wave computers to sample Boltzmann Random Variables
[article]
2021
arXiv
pre-print
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Sampling random variables following a Boltzmann distribution is an NP-hard problem involved in various applications such as training of Boltzmann machines, a specific kind of neural network. ...
We detail the various obstacles and explain the remaining difficulties in solving the sampling problem on a D-wave machine. ...
INTRODUCTION Sampling Boltzmann-distributed variables associated with an Ising model is an NP-hard problem [1] , which is notably involved in the training of the so-called Boltzmann machines (BM), a particular ...
arXiv:2111.15295v2
fatcat:ey2uzsnrwnbm3gtccht5yjvktq
Boltzmann Sampling by Degenerate Optical Parametric Oscillator Network for Structure-Based Virtual Screening
2016
Entropy
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Results of numerical simulation demonstrate that the DOPO network efficiently identifies a set of appropriate ligand molecules according to the Boltzmann sampling law. ...
This procedure takes a known structure of a protein-ligand complex as input, and structurally similar compounds with the query ligand are designed in consideration with all possible combinations of atomic ...
All degenerate states are found with almost equal probability at C/A = 0.5. ...
doi:10.3390/e18100365
fatcat:wdp7w4pl5vabvbgdqgqgazyrua
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