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 153 for “"Recurrent Neural Networks"”.
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Quantum recurrent neural networks for filtering
… This equation is transformed to fit into the neuralnetwork architecture. Each neuron in the network mediates a spatio-temporal field witha unified quantum activation function that aggregates the pdf information of theobserved signals. The activation function is the result of the solution of …
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Dynamics of adaptive recurrent neural networks
… thesis a simple, phenomenological model of a neural network with plasticity is presented in the form of a slow-fast adaptive dynamical recurrent neural network. The plasticity rule is chosen from the class of Hebbian learning rules, in which the synaptic connection between two neurons evolves …
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Time series forecasting with recurrent neural networks
… to make forecasts on time series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the field of time series forecasting. RNN is a type of specialized neural network tailored towards handling sequential data such as natural language and time series. RNN models such as LSTM …
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Dynamics and learning in recurrent neural networks
… thesis is a study of dynamics and learning in recurrent neural networks. Many computations of neural systems are carried out through a network of a large number of neurons. With massive feedback connections among these neurons, a study of its dynamics is necessary in order to understand the …
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Simulation of mental models with recurrent neural networks
Menschen sind in der Lage, mentale Modelle von Informationen, die sie aus der Umwelt aufnehmen, aufzubauen. Solche mentalen Modelle bilden die Grundlage für die Organisation und Strukturierung von sensorischer Information und Wissen und sind damit wesentlich, um Gedächtnisaufgaben ausführen zu …
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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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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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Recurrent neural networks in cognitive and vision neuroscience
… methodologies for biologically plausible neural networks, with a focus on models that incorporate recurrent dynamics characteristic of cortical circuits. First, we present an innovative approach for training stabilized supralinear networks, which are models of cortical circuits known to …
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Gauss-newton Based Learning For Fully Recurrent Neural Networks
… off-line and on-line learning approach for Fully Recurrent Neural Networks (FRNNs). The most popular algorithm for training FRNNs, the Real Time Recurrent Learning (RTRL) algorithm, employs the gradient descent technique for finding the optimum weight vectors in the recurrent neural network. …
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On the induction of temporal structure by recurrent neural networks
… on its ability to model human language. Simple Recurrent Networks (SRNs) are a class of so-called artificial neural networks that have a long history in language modelling via learning to predict the next word in a sentence. However, SRNs have also been shown to suffer from catastrophic …
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Multi-objective evolutionary neural architecture search for recurrent neural networks
Artificial neural network (ANN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) serves to automate the design of ANN architectures, and has proven to be successful in finding ANN architectures that can …
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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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Understanding computation through low-dimensional dynamics with recurrent neural networks
… little insight into how they are realized in neural network models. Addressing this disconnect requires tools for better understanding neural representations and how they are used for cognitive computations. Here, I present work towards developing a dynamical systems framework for neural …
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Utilizing Recurrent Neural Networks for Temporal Data Generation and Prediction
… of the ecosystem may be non-linear, we propose neural network-based schemes for forecasting. We create the LatentGAN architecture by extending the recurrent neural network-based ProbCast and autoencoder forecasting architectures to produce multiple forecasts for a single time series. Suites of …
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Data-Driven Decoding of Quantum Surface Codes using Recurrent Neural Networks
… remains a significant challenge. In this thesis, neural network-based decoders are explored as an approach to decode QEC data. In particular, recurrent neural network (RNN) architectures, including Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), are investigated as their are able to …
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SHIPS’ TRAJECTORIES PREDICTION USING RECURRENT NEURAL NETWORKS BASED ON AIS DATA
… and a set of context features. We use a Recurrent Neural Network model with inputs extracted from Automated Information System (AIS) data. This data includes ship coordinates, speed and course, and the ship's call sign, size, and type. These features are appropriately encoded to amplify …
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Self-orthogonalizing strategies for enhancing Hebbian learning in recurrent neural networks
A neural network model is presented which extends Hopfield's model by adding hidden neurons. The resulting model remains fully recurrent, and still learns by prescriptive Hebbian learning, but the hidden neurons give it power and flexibility which were not available in Hopfield’s original network. …
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DeepBall: Modeling expectation and uncertainty in baseball with recurrent neural networks
… knowledge. We propose DeepBall, which combines a recurrent neural network with novel regularization and ensemble aggregation. We compare this to Marcel, the industry-standard open-source baseline, and other traditional machine learning techniques, and DeepBall outperforms all. DeepBall is also …
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