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 20 of 262 for “"Unsupervised Learning"”.
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Deep unsupervised learning from speech
… In this thesis, we explore techniques for learning about speech directly from speech, with no manually generated transcriptions. Such techniques have the potential to revolutionize speech technologies for the vast majority of the world's population. The cognitive science and computer …
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Unsupervised learning of morphological forests
This thesis focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary. This formulation enables us to capture edge-wise properties reflecting single-step morphological derivations, along with global distributional properties of the …
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Localized Feature Selection For Unsupervised Learning
<p>Clustering is the unsupervised classification of data objects into different groups (clusters) such that objects in one group are similar together and dissimilar from another group. Feature selection for unsupervised learning is a technique that chooses the best feature subset for clustering. In …
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Pushing the limits of traditional unsupervised learning
Unsupervised learning has important applications in extremely large data settings such as in medical, biological, social, and environmental data. Typically in these settings, copious amounts of data are collected, with the additional burden of high dimensionality and unavailability of class labels. …
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Unsupervised learning of lexical subclasses from phonotactics
… lexica of the two languages. Moreover, the unsupervised nature of the clustering method demonstrates the learnability of sublexica from naturalistic data. The learned sublexica also replicate linguistic characterizations of actual sublexica proposed in previous literature, such as the biased …
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Toward Faster Methods in Bayesian Unsupervised Learning
… To encode structural information in these unsupervised learning problems, such as the hierarchy among words, documents, and latent topics, one can use Bayesian probabilistic models. The application of Bayesian unsupervised learning faces three computational challenges. Firstly, existing …
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Unsupervised Learning : Model-guided and Model-agnostic Approaches
Unsupervised learning is the branch of machine learning that is aimed at learning patterns from data without labels. Supervised learning with millions of labels for image classification had driven the modern deep learning revolution in the past few years. Deep neural networks have exceeded human …
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Unsupervised learning of vocal tract sensory-motor synergies
… use of dimensionality reduction algorithms in learning muscle synergies and perceptual primitives that reflect the structure in biological systems, an approach to learning sensory-motor synergies via dynamic factor analysis for control of a simulated vocal tract is presented here. This …
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Mixed Selectivity via Unsupervised Learning in Neural Networks
… and statistical role of mixed selectivity in learning complex dependencies of input stimuli. This role can be motivated from unsupervised learning of generative models, and is exhibited in increased mutual information between the stimuli and their neural representation. This argument is …
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Nonparametric Bayesian methods for supervised and unsupervised learning
… methods for solving problems of supervised and unsupervised learning. The first method simultaneously learns causal networks and causal theories from data. For example, given synthetic co-occurrence data from a simple causal model for the medical domain, it can learn relationships like "having a …
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Multiview monocular depth estimation using unsupervised learning methods
… objects. This thesis extends recent work in unsupervised methods for single-view monocular depth estimation and uses the reconstruction losses for training posed in those works. Models and baseline models were evaluated on a variety of datasets and results indicate that indicate multiview …
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Unsupervised learning and recognition of physical activity plans
… human intent, through statistical plan learning and online recognition. We approach the plan learning problem by employing unsupervised learning to automatically determine the activities in a plan based on training data. The plan activities are described by a mixture of multivariate …
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Unsupervised Learning for Generative Scene Editing and Motion
Unsupervised learning for images and videos is important for many applications in computer vision. While supervised methods usually have the best performance, the amount of data curation and labeling that supervised datasets require makes it difficult to scale. On the other hand, unsupervised …
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Unsupervised Learning of Spatiotemporal Features by Video Completion
In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as …
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Physics based supervised and unsupervised learning of graph structure
Graphs are central tools to aid our understanding of biological, physical, and social systems. Graphs also play a key role in representing and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to …
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Smart building waste monitoring system based on unsupervised learning
… (IoT) devices and maturing machine learning technologies have spawn numerous smart services permeating in every day life. These cyber-physical systems are fundamentally changing the way of managing resources, analyzing data and interacting with the physical world. The concept of …
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Unsupervised Learning Algorithm for Noise Suppression and Speech Enhancement Applications
… a speech enhancement algorithm for assisted learning devices with a single microphone, while keeping computational complexity and power consumption of the said algorithm low, is a challenging problem. There has been considerable research to solve this problem with good speech enhancement …
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Unsupervised learning of cross-modal mappings between speech and text
Deep learning is one of the most prominent machine learning techniques nowadays, being the state-of-the-art on a broad range of applications in computer vision, natural language processing, and speech and audio processing. Current deep learning models, however, rely on signicant amounts of …
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