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 9 of 9 for “"restricted boltzmann machines"”.
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Representation decomposition for knowledge extraction and sharing using restricted Boltzmann machines
Restricted Boltzmann machines (RBMs), with many variations and extensions, are an efficient neural network model that has been applied very successfully recently as a building block for deep networks in diverse areas ranging from language generation to video analysis and speech recognition. Despite …
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Using conditional restricted Boltzmann machines to generate timbral music composition systems
… application, and art. In my systems, conditional restricted Boltzmann machines (CRBM) synthesize musical timbre by learning autoregressive connections between the current output, an abstracted non-linear hidden feature layer, and past out- puts. This provides a creative space where composers can …
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Mutual Information-based RBM Neural Networks
… information theory to analyze the concatenated restricted Boltzmann machines (RBMs) and propose a mutual information-based RBM neural networks (MI-RBM). We develop a novel pretraining algorithm to maximize the mutual information between RBMs. Extensive experimental results on various …
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A model-adaptive universal data compression architecture with applications to image compression
… We then show using these implementations that Restricted Boltzmann Machines are an effective source model for compressing image data compared to other compression methods by comparing compression performance using these source models on various image datasets.
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Collective Dynamics of Interacting Cell Types
… correspondence between Hopfield networks and restricted Boltzmann machines – two classical architectures at the interface of statistical physics and machine learning. I then show that in the presence of gene regulatory noise alone, Hopfield networks encoding mammalian cell types can …
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Exploring Deep Learning Methods for Discovering Features in Speech Signals
… learnt on raw signals using Gaussian-ReLU Restricted Boltzmann Machines can achieve accuracy close to that achieved with the best traditional features. These features are, however, learnt using a generative model that ignores domain knowledge. We develop methods to discover features that …
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Vision-based human action recognition using machine learning techniques
… method employs Deep Belief Networks (DBNs) with restricted Boltzmann machines for action recognition in unconstrained videos. The proposed method automatically extracts suitable feature representation without any prior knowledge using unsupervised deep learning model. The effectiveness of the …
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Deep neural network acoustic models for multi-dialect Arabic speech recognition
… modelling system for Arabic speech recognition. Restricted Boltzmann Machines (RBMs) DNN models have not been explored for any Arabic corpora previously. This allows us to claim priority for adopting this RBM DNN model for the Levantine Arabic acoustic models. A post-processing enhancement was …
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COMBINING QUANTUM TRAJECTORIES AND TIME-DEPENDENT VARIATIONAL MONTE CARLO FOR MANY-BODY OPEN QUANTUM SYSTEMS
Simulating quantum systems is complex due to the “curse of dimensionality”, which is exacerbated in open quantum systems that interact with their environment. Indeed, tra- ditional computational methods struggle with the exponential growth of Hilbert space in these systems. This thesis introduces …