Prediction of ICU in-hospital mortality using a deep Boltzmann machine and dropout neural net

Shape completion is an important task in the field of image processing. An alternative method is to capture the shape information and finish the completion by a generative model, such as Deep Boltzmann Machine. With its powerful ability to deal with the distribution of the shapes, it is quite easy to acquire the result by sampling from the model. In this paper, we make use of the hidden activation of the DBM and incorporate it with the convolutional shape features to fit a regression model. We compare the output of the regression model with the incomplete shape feature in order to set a proper and compact mask for sampling from the DBM. The experiment shows that our method can obtain realistic results without any prior information about the incomplete object shape.

Download Full-text

Reinforcement learning using quantum Boltzmann machines

Quantum Information and Computation ◽

10.26421/qic18.1-2-3 ◽

2018 ◽

Vol 18 (1&2) ◽

pp. 51-74 ◽

Cited By ~ 2

Author(s):

Daniel Crawford ◽

Anna Levit ◽

Navid Ghadermarzy ◽

Jaspreet S. Oberoi ◽

Pooya Ronagh

Keyword(s):

Reinforcement Learning ◽

Learning Algorithm ◽

Transverse Field ◽

Boltzmann Machine ◽

Quantum Annealing ◽

Restricted Boltzmann Machines ◽

Boltzmann Machines ◽

Ising Spin ◽

Learning Tasks ◽

Deep Boltzmann Machine

We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations show that DBMs are trained more effectively than restricted Boltzmann machines (RBM) with the same number of nodes. We then develop a framework for training the network as a quantum Boltzmann machine (QBM) in the presence of a significant transverse field for reinforcement learning. This method also outperforms the reinforcement learning method that uses RBMs.

Download Full-text

Prediction of ICU in-hospital mortality using a deep Boltzmann machine and dropout neural net

A New Method to Enhance Fingerprint Image Reconstruction Using Deep Boltzmann Machine

Integration of Deep Boltzmann Machine and Generalized Radial Basis Function Network for Photovoltaic Generation Output Forecasting

Contractive Slab and Spike Convolutional Deep Boltzmann Machine

Compliance Framework for Seizure Detection via Gaussian Deep Boltzmann Machine Using EEG Data Signal

AdS/CFT correspondence as a deep Boltzmann machine

Mutlimodal Learning with Deep Boltzmann Machine for Emotion Prediction in User Generated Videos

Emotion recognition from thermal infrared images using deep Boltzmann machine

Gaussian-Bernoulli deep Boltzmann machine

Shape Completion Using Deep Boltzmann Machine

Reinforcement learning using quantum Boltzmann machines

Export Citation Format