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Full-Text Articles in Engineering
Evaluating Neural Network Decoder Performance For Quantum Error Correction Using Various Data Generation Models, Brett M. Martin
Evaluating Neural Network Decoder Performance For Quantum Error Correction Using Various Data Generation Models, Brett M. Martin
Theses and Dissertations
Neural networks have been shown in the past to perform quantum error correction (QEC) decoding with greater accuracy and efficiency than algorithmic decoders. Because the qubits in a quantum computer are volatile and only usable on the order of milliseconds before they decohere, a means of fast quantum error correction is necessary in order to correct data qubit errors within the time budget of a quantum algorithm. Algorithmic decoders are good at resolving errors on logical qubits with only a few data qubits, but are less efficient in systems containing more data qubits. With neural network decoders, practical quantum computation …
Extractive Text Summarization On Single Documents Using Deep Learning, Shehab Mostafa Abdel-Salam Mohamed
Extractive Text Summarization On Single Documents Using Deep Learning, Shehab Mostafa Abdel-Salam Mohamed
Theses and Dissertations
The task of summarization can be categorized into two methods, extractive and abstractive summarization. Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches such as statistical-based, graph-based, and deep-learning based approaches. Deep learning has achieved promising performance in comparison with the classical approaches and with the evolution of neural networks such as the attention network or commonly known as the Transformer architecture, there are potential areas for …
Hyper-Parameter Optimization Of A Convolutional Neural Network, Steven H. Chon
Hyper-Parameter Optimization Of A Convolutional Neural Network, Steven H. Chon
Theses and Dissertations
In the world of machine learning, neural networks have become a powerful pattern recognition technique that gives a user the ability to interpret high-dimensional data whereas conventional methods, such as logistic regression, would fail. There exists many different types of neural networks, each containing its own set of hyper-parameters that are dependent on the type of analysis required, but the focus of this paper will be on the hyper-parameters of convolutional neural networks. Convolutional neural networks are commonly used for classifications of visual imagery. For example, if you were to build a network for the purpose of predicting a specific …
Cyber Data Anomaly Detection Using Autoencoder Neural Networks, Spencer A. Butt
Cyber Data Anomaly Detection Using Autoencoder Neural Networks, Spencer A. Butt
Theses and Dissertations
The Department of Defense requires a secure presence in the cyber domain to successfully execute its stated mission of deterring war and protecting the security of the United States. With potentially millions of logged network events occurring on defended networks daily, a limited staff of cyber analysts require the capability to identify novel network actions for security adjudication. The detection methodology proposed uses an autoencoder neural network optimized via design of experiments for the identification of anomalous network events. Once trained, each logged network event is analyzed by the neural network and assigned an outlier score. The network events with …