scholarly journals Restricted Boltzmann Machines for Fundus Image Reconstruction and Classification of Hypertension Retinopathy

2021 ◽  
Vol 17 (2) ◽  
pp. 156-166
Author(s):  
Bambang Krismono Triwijoyo ◽  
Boy Subirosa Sabarguna ◽  
Widodo Budiharto ◽  
Edi Abdurachman
Keyword(s):  
Image Reconstruction ◽  
Fundus Image ◽  
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 Restricted Boltzmann Machines for Classification of Hepatocellular Carcinoma

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
James A. Koziol ◽  
Eng M. Tan ◽  
Liping Dai ◽  
Pengfei Ren ◽  
Jian-Ying Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cancer Detection ◽  
Classification Problem ◽  
Classification Problems ◽  
Operating Characteristics ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Multiple Antigen ◽  
Detection And Diagnosis

Multiple antigen miniarrays can provide accurate tools for cancer detection and diagnosis. These miniarrays can be validated by examining their operating characteristics in classifying individuals as either cancer patients or normal (non-cancer) subjects. We describe the use of restricted Boltzmann machines for this classification problem, relative to diagnosis of hepatocellular carcinoma. In this setting, we find that its operating characteristics are similar to a logistic regression standard and suggest that restricted Boltzmann machines merit further consideration for classification problems.

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