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 29 for “"Deep neural networks (DNN)"”.
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Investigation of KimiaNet's and DenseNet's deep features in lung cancer subtypes
Deep neural networks (DNN) have extended applications in the _eld of digital pathology. One of which is to act as feature extractors for content-based image retrieval (CBIR) systems. Therefore, it is necessary to investigate how these deep features work and attribute these features to histologic …
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Learning Hyperparameters for Inverse Problems by Deep Neural Networks
… work, we will describe new approaches that use deep neural networks (DNN) to estimate these regularization parameters. We will train multiple networks to approximate mappings from observation data to individual regularization parameters in a supervised learning approach. Once the networks are …
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A Geospatial and Machine Learning Framework for Forecasting Ground Level Ozone Pollution
… forecasting and estimation. Of these AI methods, deep neural networks (DNN) have demonstrated the highest accuracy due to their ability extract non-linear relationships from high dimensional, noisy data inputs.</p> <p>This research effort uses novel data sources, namely NOAA’s High Resolution …
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Regulating Orthogonality Of Feature Functions For Highly Compressed Deep Neural Networks
When designing deep neural networks (DNN), the number of nodes in hidden layers can have a profound impact on the performance of the model. The information carried by the nodes in each layer creates a subspace, whose dimensionality is determined by the number of nodes and their linear dependency. …
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One-Shot Learning Model for Cancer Diagnosis from Histopathological Images
… automated image analysis techniques using Deep Neural Networks (DNN) have been proposed for analyzing histopathology images for various cancer types and datasets. Typical challenges for a deep neural network to operate in this setting are limited datasets, gigapixel images and small …
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Optimization, Learning, and Control for Energy Networks
Massive infrastructure networks such as electric power, natural gas, or water systems play a pivotal role in everyday human lives. Development and operation of these networks is extremely capital-intensive. Moreover, security and reliability of these networks is critical. This work identifies and …
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Accelerating distributed neural network training with network-centric approach
Distributed training of Deep Neural Networks (DNN) is an important technique to reduce the training time of large DNNs for a wide range of applications. In existing distributed training approaches, however, the communication time to periodically exchange parameters (i.e., weights) and gradients …
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DomainSweep: Input Domain Driven Falsification of Cyber-Physical Systems
… incorporate AI-enabled controllers based on deep neural networks (DNN). This growth necessitates robust safety measures and reliable protocols that ensure these systems function correctly. However, as these systems grow in complexity and scale, traditional verification methods become limited. …
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Learning to solve problems in computer vision with synthetic data
Deep neural networks (DNN) have become the tool of choice for many researchers due to their superior performance. However, for DNNs to reach their full potential, a large enough dataset must be available. This poses severe limitation over problems that DNN can be applied to. Fortunately, many …
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Dynamic Modeling and Real-time Simulation of Power Electronics-dominated Power Grids Using Hybrid DDM and EDDM Techniques
… Machine Learning (SciML). It introduces Extended DeepDDM, a pioneering approach using deep neural networks (DNN) to discretize subproblems from DDMs for solving partial differential equations (PDEs). By incorporating initial condition and ODE residual loss terms, it enhances optimization and …
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Algorithm Hardware Codesign for High Performance Neuromorphic Computing
… on these power limited scenarios. Though deep learning has achieved impressive performance on various realistic and practical tasks such as anomaly detection, pattern recognition, machine vision etc., the ever-increasing computational complexity and model size of Deep Neural Networks (DNN) …
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Enhancing Communications Aware Evasion Attacks on RFML Spectrum Sensing Systems
… techniques such as reinforcement learning and deep neural networks (DNN) can be leveraged to improve upon traditional wireless communications methods so that they no longer require expertly-defined features. Simultaneously, cybersecurity and electronic warfare are growing areas of focus and …
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Efficient Continual Learning and On-Device Training for Mobile and IoT Devices
… a pivotal role in the widespread adoption of deep neural networks (DNN) to support various real-world scenarios in mobile computing, including personalising user experiences and enabling adaptive household robots. Such use cases require DNNs to continuously learn and adapt to changing …
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Accelerated deep learning for the edge-to-cloud continuum: A specialized full stack derived from algorithms
… been a major driver for the rapid evolution of Deep Neural Networks (DNN). Due to their insatiable demand for compute power, naturally, both the research community as well the industry have turned to accelerators to accommodate modern DNN computation. Furthermore, DNNs are gaining prevalence and …
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Experimental characterization and machine learning optimization of polymer nanocomposite membranes for carbon capture systems
… applied three machine learning (ML) techniques, Deep Neural Networks (DNN), Random Forest (RF), and XGBoost models, to analyze the CO2 permeability and CO2/N2 selectivity of nanocomposite membranes. The datasets for CO2/N2 separation were sourced from our experimental results and the published …
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Multi-Dimensional QoS and Collaborative MAC Layer Design for Dense, Diverse, and Dynamic IoT Network
… Points (APs) of future Wireless Fidelity (Wi-Fi) networks are expected to support dense Stations (STAs) with diverse Quality-of-Service (QoS) requirements under dynamic channel conditions. On account of high access collision and optimization problem complexity, the performance degradation brings …
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Camera Spatial Frequency Response Derived from Pictorial Natural Scenes
… chart signal. Further, with the increased use of Deep Neural Networks (DNN) for image recognition tasks and autonomous vision systems, there is an increased need for monitoring system performance outside laboratory conditions in real-time, i.e. live-MTF. Such measurements would assist in …
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Person Re-identification and an Adversarial Attack and Defense for Person Re-identification Networks
… time-stamps. </p><p>With the recent advances in deep neural networks (DNN), the state-of-the-art performance of person ReID has been improved significantly. However, latest works in adversarial machine learning have shown the vulnerabilities of DNNs against adversarial examples, which are …
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Person Re-identification And An Adversarial Attack And Defense For Person Re-identification Networks
… time-stamps. </p><p>With the recent advances in deep neural networks (DNN), the state-of-the-art performance of person ReID has been improved significantly. However, latest works in adversarial machine learning have shown the vulnerabilities of DNNs against adversarial examples, which are …
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Application of density functional theory and machine learning in the prediction of efficient catalysts for the oxidative coupling of methane with reduced CO2 production at low temperature
… a combination of catalyst electronic properties, deep neural networks (DNN) configured as deep feed-forward networks with back-propagation, along with random forest regression (RFR), support vector regression (SVR) and extreme gradient boost regression (XGBR), were compared on the basis on their …
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