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 30 for “"Multi-modal Data"”.
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Classifying Sidewalk Materials Using Multi-Modal Data
… specifically designed for the classification of multi-modal sidewalk materials. The proposed framework aims to empower individuals with BLV to automatically gather information about sidewalk materials while navigating their surroundings. This innovative solution comprises two primary components. …
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Minimum description length, regularisation and multi-modal data
… that has a rough interpretation as the number of data points fit by the model. Not concerned with finding optimal descriptions, the cost function prefers to form minimum descriptions in a naive way for computational convenience. The cost function is called the Naive Description Length cost …
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Model-based approaches for learning control from multi-modal data
… using popular RL methods, learning a model from data and performing online planning in the form of model predictive control (MPC) can be much more data-efficient and practical for deploying on real robotics systems. In addition to a generally applicable sample-based planning strategy, another …
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Machine Learning Approaches to Multi-Modal Data Integration and Translation in Single-Cell Biology
… to integrate and translate between single-cell data. In the first half, I develop methods based on generative modeling, representation learning and optimal transport to learn mappings between cells collected at different time points. In the second half, I develop methods based on generative …
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Dynamic graph neural network framework for real-time multi-modal data analysis and predictive modeling
… prominent for analyzing complex, interconnected data across fields such as transportation, social networks, and cybersecurity. Despite their advancements, many existing GNN models struggle to capture the intricate interactions among temporal, spatial, and domain-specific knowledge, particularly …
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Careful Design: Using multi-modal data and virtual reality to bridge the subjectivity gap in architectural space-making.
Architecture is a field that deals with the synthesis of many others. It is not just design and construction, but philosophy, art, technology, culture, user experience and all the intangible aspects of the human psyche. As such, architects, throughout their training and professional life, aim to …
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Multi-Modal Data Fusion, Image Segmentation, and Object Identification using Unsupervised Machine Learning: Conception, Validation, Applications, and a Basis for Multi-Modal Object Detection and Tracking
… While the increase in instruments, and therefore datasets, is a boon for those aiming to study the complexities of the various Earth systems, it can also present a large number of new challenges. With this information in mind, our group has set our aims on combining datasets with different spatial …
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Visual Analytics and Interactive Machine Learning for Human Brain Data
… applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery. It mainly consists of two parts: multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate …
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Efficient Distributed and Multi-Modal Machine Learning in Wireless Networks
… and the scarce and private nature of wireless data. First, ML models at the application layer, e.g., on-device AI, often require private data from distributed devices. One can resort to distributed ML algorithms such as federated learning (FL) by communicating only ML model parameters over …
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Machine-learning-enabled optimization and online monitoring for efficient and high-quality smart drying
… scale drying processes and systems involve multiple interacting process parameters, conflicting production objectives, and highly uncertain sample characteristics, which make process control extremely challenging. Current industrial practice lacks the necessary decision-making tools to …
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Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells
… to explore the ability of Raman microscopy data to infer cell states in microbes and the gene expression values of ten genes in mouse embryonic fibroblasts (MEFs) undergoing a dynamic cellular reprogramming process. Using a multi-modal, supervised learning approach, we provide evidence that …
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Learning Audio-Video Language Representations
… due to the reliance on manually annotated speech data. Unlabeled multi-modal data, such as videos, are now increasingly available in many different languages and provide opportunities to scale speech technologies. In this thesis, we introduce models and datasets for learning visually grounded …
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Permutation-based Significance Tests for Multi-modal Hierarchical Dirichlet Processes with Application to Audio-visual Data
Complex underlying distributions in multi-modal data motivate the need for data fusion methods that integrate observations of different modalities in a meaningful way. We explore the multi-modal hierarchical Dirichlet process (mmHDP) mixture model as a Bayesian non-parametric approach to data …
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Graph-Based Approach: Bridging Insights from Structured and Unstructured Data
… intricate relationships and patterns in complex data, enabling the integration of structured and unstructured information for insightful decision-making across diverse domains. Our research focuses on constructing graphs from structured and unstructured data, demonstrating their applications in …
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Learning from multi-modal spatiotemporal data: machine learning approaches to advance resilience in smart grids
… operators. The rapid growth of technology and data storage allowed the deployment of sensing devices across the electric grid. Such technologies present a golden opportunity to tackle many of the electric grid's challenges. Despite that, such technologies presented many challenges …
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Modelling brain tumours with network neurosciences and deep learning
… of estimating brain networks in retrospective datasets with only anatomical MRI. In Chapter 5, I combined the prior knowledge of brain network disruption and the clinical significance of tumour geometrics to develop a multi-modal deep learning framework that learnt from brain networks, tumour …
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Learning without Expert Labels for Multimodal Data
… due to the availability of large-scale labeled datasets, obtaining labeled datasets at the required granularity is challenging in many real-world applications, especially in scientific domains, due to the costly and labor-intensive nature of generating annotations. Hence, there is a need to …
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Multi-modal and deep learning for robust speech recognition
… First, we developed an ASR system using multi-channel information from microphone arrays via accurate speaker tracking with Kalman filtering and subsequent beamforming. The system was evaluated on the publicly available Reverb Challenge corpus, and placed second (out of 49 submitted …
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Predicting Changes in Individual Wellbeing Scores: Mixed Effects Models using Sleep Data from Wearables
… inadequately explored. The nature of sleep data poses challenges in capturing and interpreting temporal patterns, but the growing popularity of wearable devices capable of collecting vast multi-modal data presents a promising avenue to bridge this gap. In this thesis, the aim is two-fold: …
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