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 65 for “"sequential data"”.
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Generative modeling of sequential data
… generative modeling, with a special emphasis on sequential data. Namely, we develop methodologies to deal with issues regarding representation (modeling choices), learning paradigm (e.g. maximum likelihood, method of moments, adversarial training), and optimization. For the representation aspect, …
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Motif discovery in sequential data
… methods for the automated discovery of motifs in sequential data. These data include DNA sequences, protein sequences, and real-valued sequential data such as protein structures and timeseries of arbitrary dimension. As more genomes are sequenced and annotated, the need for automated, …
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Deep learning in sequential data analysis
… to look into deep learning based methods for sequential data such as videos and medical image sequences. With the extra information from its additional sequential dimension, sequential data naturally raises an important and challenging question: How can we effectively and efficiently integrate …
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Representation learning for non-sequential data
… representations for sets and graphs. Typically, data collections in machine learning problems are structured as arrays or sequences, with sequential relationships between successive elements. Sets and graphs both break this common mold of data collections that have been extensively studied in the …
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Domain generalization for sequential data via invariant subspace recovery
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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General-purpose compression for sequential data using recurrent neural networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
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On Modeling Dependency Dynamics of Sequential Data: Methods and Applications
Information mining and knowledge learning from sequential data is a field of growing importance in both industrial and academic fields. Sequential data, which is the natural representation format of the information flow in many applications, usually carries enormous information and is able to help …
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Discovering E-commerce Sequential Data Sets and Sequential Patterns for Recommendation
… system accuracy will be improved if more complex sequential patterns of user purchase behavior are learned and included in its user-item matrix input, to make it more informative before collaborative filtering. Existing recommendation systems that use mining techniques with some sequences are …
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Information-centric Algorithms for Feature Extraction in High-Dimensional Sequential Data
Hidden Markov Models (HMMs) are a cornerstone of sequential data analysis, offering a robust framework for modeling observable events influenced by hidden internal states. With applications spanning speech recognition, video analysis, bioinformatics, and financial time series, HMMs enable the …
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Sequential data inference via matrix estimation : causal inference, cricket and retail
… variation across multiple instances of sequential data. Examples of such data include sales of a product over a period of time across several retail locations; trajectories of scores across cricket games; and annual tobacco consumption across the United States over a period of decades. A …
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Neural Sequence Modeling for Domain-Specific Language Processing: A Systematic Approach
… architecture and enormous open access online data, large-scale pre-trained language models have shown great modeling and generalization capacity for sequential data. However, not all domains benefit equally from the rapid development of neural sequence modeling. Domains like healthcare and …
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The First Year of Marriage: Dyadic Adjustment and Intimacy
… through the first year of marriage. A cross-sequential data collection strategy was used to measure change in these processes over time. The data were also analyzed for gender differences.
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Statistical Learning for Sequential Unstructured Data
Unstructured data, which cannot be organized into predefined structures, such as texts, human behavior status, and system logs, often presented in a sequential format with inherent dependencies. Probabilistic model are commonly used to capture these dependencies in the data generation process …
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Time series forecasting with recurrent neural networks
… such as demand trends, stock prices, and sensor data, is an essential data type in our modern world. Over the years, many models such as Exponential Smoothing and ARIMA are developed to make forecasts on time series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the field of …
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Physics-informed gated recurrent unit neural networks model for surface temperature estimation of Lithium-ion batteries
… neural networks (PINNs). The GRU processes sequential data voltage, current, and ambient temperature capturing dynamic battery behavior, while the physics-informed layers embed critical physical parameters, including electrical, thermal, and heat generation models, during training. Validated …
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Magister - Metodologia de análise de programas de educação à distância baseada em Learning Analytics
The increasing of the data registered in courses offered in the distance modality boost the use of computational methods adapted to the research and the grouping of educational data, aiming to discover learning behaviors patterns. This research area allows the development of automated monitoring, …
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Higher Order Recurrent Neural Network for Language Modeling
… to better model long term dependency in sequential data. We propose to use more memory units to keep track of more preceding states in recurrent neural networks (RNNs), which are all recurrently fed to the hidden layers as feedback through different weighted paths. By extending the …
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