A deep fusion model based on restricted Boltzmann machines for traffic accident duration prediction

2020 ◽  
Vol 93 ◽  
pp. 103686 ◽  
Author(s):  
Linchao Li ◽  
Xi Sheng ◽  
Bowen Du ◽  
Yonggang Wang ◽  
Bin Ran
Keyword(s):  
Traffic Accident ◽  
Fusion Model ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Model Based ◽  
Duration Prediction

Using Conditional Restricted Boltzmann Machines to Model Complex Consumer Shopping Patterns

2019 ◽  
Vol 38 (4) ◽  
pp. 711-727 ◽  
Author(s):  
Feihong Xia ◽  
Rabikar Chatterjee ◽  
Jerrold H. May
Keyword(s):  
Data Sets ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Model Based ◽  
Model Complex ◽  
Purchase Patterns

We develop and apply a model based on conditional restricted Boltzmann machines to analyze intertemporal crossproduct purchase patterns in enormous consumer purchase data sets.

scholarly journals Skull segmentation from MR scans using a higher-order shape model based on convolutional restricted Boltzmann machines

2018 ◽  
Author(s):  
Koen Van Leemput ◽  
Jesper D. Nielsen ◽  
Christian Bauer ◽  
Hartwig Siebner ◽  
Kristoffer H. Madsen ◽  
...  
Keyword(s):  
Higher Order ◽  
Restricted Boltzmann Machines ◽  
Shape Model ◽  
Boltzmann Machines ◽  
Model Based

scholarly journals Analysis on Noisy Boltzmann Machines and Noisy Restricted Boltzmann Machines

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wenhao Lu ◽  
Chi-Sing Leung ◽  
John Sum
Keyword(s):  
Restricted Boltzmann Machines ◽  
Boltzmann Machines

scholarly journals Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guanglei Xu ◽  
William S. Oates
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Image Reconstruction ◽  
Image Recognition ◽  
Shannon Entropy ◽  
Reconstruction Error ◽  
Likelihood Method ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
D Wave

AbstractRestricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter ($$\beta $$ β ) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

A novel fusion model based online state of power estimation method for lithium-ion capacitor

2021 ◽  
Vol 36 ◽  
pp. 102387
Author(s):  
Wenxin Chen ◽  
Cheng Xu ◽  
Manlin Chen ◽  
Kai Jiang ◽  
Kangli Wang
Keyword(s):  
Estimation Method ◽  
Lithium Ion ◽  
Power Estimation ◽  
Fusion Model ◽  
Model Based ◽  
Lithium Ion Capacitor ◽  
State Of Power
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