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 15 of 15 for “"Restricted Boltzmann Machine"”.
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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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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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Neural ProbabilisticModels for Melody Prediction, Sequence Labelling and Classification
… known as the Recurrent Temporal Discriminative Restricted Boltzmann Machine (RTDRBM), was introduced in the process and found to outperform the rest of the models. A generalisation of this modelling task was also explored, and involved extending the set of musical features used as input by the …
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Using conditional restricted Boltzmann machines to generate timbral music composition systems
Machine-learning models have been successfully applied to musical composition in a variety of forms, including audio classification, recognition, and synthesis. The capability of algorithms to learn complex musical elements allows composers to more deeply investigate the development of their …
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Provable Algorithms for Learning and Variational Inference in Undirected Graphical Models
… reasoning, with numerous applications across machine learning and the sciences. This thesis deals with algorithmic and statistical problems of learning a high-dimensional graphical model from samples, and related problems of performing inference on a known model, both areas of research which …
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Online Non-linear Prediction of Financial Time Series Patterns
We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data …
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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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Interdisciplinary Studies of Complex Network and Machine Learning and Its Applications
… network, to Random Markov Field and Ising Model, Boltzmann and Restricted Boltzmann machine and the algorithm of Belief Propagation. Last but not the least, we introduce the most widely used neural network family and its two main types: Convolutional Neural Network and Recurrent Neural …
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Conditional Neural Networks for Speech and Language Processing
… success in several real world tasks: from machine translation to web recommendation, and it is also greatly improving the computer vision and the natural language processing. Compared with conventional machine learning techniques, neural network based deep learning do not require careful …
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Open quantum system theory from an information perspective
… on the most recent theoretical developments, machine learning implementations in the form of artificial neural networks. We explore all of these techniques with respect to their compression efficiency, performance and representational limits by applying them to a variety of physical setups and …
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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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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 …