Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"Restricted Boltzmann Machine (RBM)"”.
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Identifying Cancer Subtypes Using Unsupervised Deep Learning
… present a pathway-based clustering method using Restricted Boltzmann Machine (RBM), called R-PathCluster, for identifying unknown subtypes with pathway markers of gene expressions. In order to assess the performance of R-PathCluster, we conducted experiments with several clustering methods such …
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Missing data imputation in a clinical registry with deep generative models
… the explosion of data and the advancement in the machine learning techniques, more advanced deep generative models have shown the ability to learn complex distributions in high dimensional space. In this work, we explored two deep generative models, Restricted Boltzmann Machine (RBM) and …
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Study of Critical Phenomena with Monte Carlo and Machine Learning Techniques
… Dissertation is devoted to the applications of Machine Learning models in physical systems. First, we show that a trained Convolutional Neural Network (CNN) using configurations from the Ising model with conserved magnetization is able to find the location of the critical point. Second, using as …
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Investigating Topological Quantum Matter: Machine Learning Topological Phases, Topological Quantum Codes, Interplay of Disorder and Topology via Transport Phenomena and Phase Transitions
… model into a neural framework by training a restricted Boltzmann machine (RBM) on stochastic-reconfiguration Monte-Carlo data. A custom PyTorch code with translation projection, split real/imaginary learning rates, and polynomial pre-training reaches ground state energies within 0.09% of …
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FPGA implementation of a Restricted Boltzmann Machine for handwriting recognition
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-05-01
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Learning Semantic Information from Multimodal Data using Deep Neural Networks
… value from Big data is Deep learning, a type of machine learning algorithm inspired by the structure and function of the human brain called artificial neural networks that learn from large amounts of data. Deep learning has been widely used and applied in many research fields such as natural …
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AB-INITIO DYNAMICS IN STRONGLY INTERACTING MANY-BODY SYSTEMS
… quantum states (NNQS), which employs machine learning to represent the variational wave function. In the first part of this work, encompassing the first four chapters, we extend the t-VMC to the class of shadow wave functions (SWF) [3, 4], that incorporate many-body correlations by …