Learning Entry Profiles of Children with Autism from Multivariate Treatment Information Using Restricted Boltzmann Machines

2015 ◽  
pp. 245-257 ◽  
Author(s):  
Pratibha Vellanki ◽  
Dinh Phung ◽  
Thi Duong ◽  
Svetha Venkatesh
Keyword(s):  
Children With Autism ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Treatment Information

scholarly journals Classifying a type of brain disorder in children: an effective fMRI based deep attempt

2021 ◽  
Vol 22 (1) ◽  
pp. 260
Author(s):  
Abeer M Mahmoud ◽  
Hanen Karamti
Keyword(s):  
Neural Networks ◽  
Children With Autism ◽  
Learning Approaches ◽  
Restricted Boltzmann Machines ◽  
Brain Disorder ◽  
Boltzmann Machines ◽  
Learning Framework ◽  
Straight Line ◽  
Healthy Control ◽  
The Brain

<span>Recent advanced intelligent learning approaches that are based on using neural networks in medical diagnosing increased researcher expectations. In fact, the literature proved a straight-line relation of the exact needs and the achieved results. Accordingly, it encouraged promising directions of applying these approaches toward saving time and efforts. This paper proposes a novel hybrid deep learning framework that is based on the restricted boltzmann machines (RBM) and the contractive autoencoder (CA) to classify the brain disorder and healthy control cases in children less than 12 years. The RBM focuses on obtaining the discriminative set of high guided features in the classification process, while the CA provides the regularization and the robustness of features for optimal objectives. The proposed framework diagnosed children with autism recording accuracy of 91, 14% and proved enhancement compared to literature.</span>

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.

scholarly journals Predicting drug-target interactions using restricted Boltzmann machines

2013 ◽  
Vol 29 (13) ◽  
pp. i126-i134 ◽  
Author(s):  
Yuhao Wang ◽  
Jianyang Zeng
Keyword(s):  
Drug Target ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines
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