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 21 for “"recurrent neural networks (RNNs)"”.
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Modeling of electrical circuit with recurrent neural networks
… a circuit modeling methodology using recurrent neural networks (RNNs) is developed. The methodology covers model structure selection, data generation, training, and model implementation for circuit simulation. Several different RNN structures are investigated and their capabilities in …
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Higher Order Recurrent Neural Network for Language Modeling
In this thesis, we study novel neural network structures 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 …
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Physical symmetry enhanced neural networks
… change our world. Today's AI technique relies on neural networks. In this thesis, we propose several physical symmetry enhanced neural network models. We first developed unitary recurrent neural networks (RNNs) that solve gradient vanishing and gradient explosion problems. We propose an efficient …
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Lung cancer malignancy predication with recurrent neural networks
… diagnosis (CAD) has applied convolutional neural networks (CNNs) to detect and classify nodules in CT scans, with the goal of assisting radiologists diagnose lung cancer. In the past decade, new screening pro- tocols have been enacted that advise high-risk patients to get annual CT …
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Uncertainty Quantification in Deep Learning Models of G-Computation for Outcome Prediction under Dynamic Treatment Regimes
G-Net is a neural network framework that implements g-computation, a causal inference method for making counterfactual predictions and estimating treatment effects under dynamic and time-varying treatment regimes. Two G-Net models have been successfully implemented: one that uses recurrent neural …
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Crime Detection from Pre-crime Video Analysis
… diverse set of models including 3D Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and a specially developed transformer architecture, the research systematically explores the impact of integrating additional contextual information into video …
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A Cost-Efficient Digital ESN Architecture on FPGA
… capabilities rely on the dynamical behavior of recurrent neural networks (RNNs). Its performance metrics outperform traditional RNNs in nonlinear system identification and temporal information processing. In this thesis, we design and implement ESNs through Field-programmable gate array (FPGA) …
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Forecasting Energy Consumption using Sequence to Sequence Attention models
… used for energy forecasting, such as FeedForward Neural Networks, are not well-suited for interpreting the time dimensionality of a signal. Consequently, this thesis applies Sequence-to-Sequence (S2S) Recurrent Neural Networks (RNNs) with attention for electrical load forecasting. The S2S and S2S …
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RNA secondary structure prediction using hybrid methods
… the encoder-decoder transformer (S2ED), leverage recurrent neural networks (RNNs) with Dynamic Programming (DP) and an encoder-decoder network with a subset prediction formulation, respectively, to achieve efficient global structure assembly. The proposed framework exhibits high accuracy on the …
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Scalable and Reconfigurable True-Time Delay Line for Integrated Radio-Frequency Recurrent Neural Processors
… and tunable delay-line architecture for analog recurrent neural networks (RNNs) operating directly in the RF-domain. Previous research has shown the RF-domain RNNs are capable of performing real-time anomaly detection in wireless systems while reducing the inference latency of the wireless …
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Text readability and summarisation for non-native reading comprehension
… a similarity matrix and apply a convolutional neural network (CNN) model to assess the summary quality using the similarity matrix. In the third approach, we build an end-to-end summarisation assessment model using recurrent neural networks (RNNs). Further, we combine the three approaches to a …
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Efficient and Scalable Deep Learning
<p>Deep Neural Networks (DNNs) can achieve accuracy superior to traditional machine learning models, because of their large learning capacity and the availability of large amounts of labeled data. In general, larger DNNs can obtain higher accuracy. However, there are two obstacles which hinder us …
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Enhancing Interpretability: The Role of Concept-based Explanations Across Data Types
Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. Unfortunately, they have been shown to be black-boxes whose behaviour cannot be understood directly. Thus, a key step to further empowering DNN-based approaches is improving their explainability, which is …
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Interpretable Deep Learning for Time Series
… in such fields are hesitant to use Deep Neural Networks (DNNs) that can be difficult to interpret. For example, in clinical research, one might ask, ``Why did you predict this person as more likely to develop Alzheimer's disease?". As a result, research efforts to improve the …
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From big data to personal narratives: a supervised learning framework for decoding the course of traumatic brain injury in intensive care
… modelling. This thesis combines a range of neural network (NN) architectures to propose a methodological framework by which all of the CENTER-TBI data collected before and during a patient's ICU stay can be dynamically mapped to ordinal endpoints. All of the CENTER-TBI variables are …
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Astrometric and Photometric Data Fusion in Machine Learning-Based Characterization of Resident Space Objects
… come. The third portion of this work uses deep recurrent neural networks (RNNs), where the feasibility of recovering orbital regime from astrometric data of RSOs is explored for 32 different orbital families that arise naturally in the circular restricted three body problem (CR3BP) framework of …
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Improving Abstractive Summarization and Information Consistency Assessment
… more traditional approaches. Initially, these neural-based models were trained from scratch, with randomly initialized parameters, relying solely on supervised training. As this meant there was usually limited training data, the generated summaries often had limited diversity and fluency. The …
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Energy Efficient Deep Spiking Recurrent Neural Networks: A Reservoir Computing-Based Approach
Recurrent neural networks (RNNs) have been widely used for supervised pattern recognition and exploring the underlying spatio-temporal correlation. However, due to the vanishing/exploding gradient problem, training a fully connected RNN in many cases is very difficult or even impossible. The …
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Historical Consistent Neural Networks for Wind Power Prediction
… and long-range temporal dependencies. While Recurrent Neural Networks (RNNs) are widely used for such tasks, they often suffer from temporal inconsistency: relying on external inputs during training that are unavailable during forecasting. Historical Consistent Neural Networks (HCNNs) offer a …
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A cryptographic approach to location privacy
… or utilise more sophisticated techniques like recurrent neural networks (RNNs) to model the evolution of user mobility behaviour over time. The second part of the thesis addresses privacy concerns in indoor Wi-Fi localisation. We propose a privacy-preserving protocol that uses partial …
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