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 253 for “"Deep Neural Network"”.
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Deep Neural Network for Anomaly Detection
The rapid growth in diverse network devices (e.g., Internet of Things/IoT devices) and new cyber-physical systems (CPSs) services create new surfaces for cyberattacks. To safeguard these CPSs, anomaly detection (AD) that detects potential attacks/adversarial behaviors plays a pivotal role. This …
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Noise-Robust Speech Recognition Using Deep Neural Network
… the noise robustness of the recently developed Deep Neural Networks (DNNs) based speech recognition systems. Five techniques have been proposed. Firstly, a Mean Variance Normalization technique was developed to integrate noise statistics using Vector Taylor Series. Secondly, a Deep Split …
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On Energy Modeling of Deep Neural Network Operations
The fast broader adoption of ML applications has caused a surge in their global energy usage, necessitating a comprehensive understanding of the tradeoffs between execution speed and energy consumption. While previous work was focused on time-only or inference-only studies, we provide a more …
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Transform Domain Deep Neural Network Layers and Their Applications
… that integrates orthogonal transform theory with deep neural network architectures to achieve efficient data compression, representation learning, and image correction. The motivation stems from the increasing demand for accurate and resource-efficient data processing in biomedical and industrial …
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Deep neural network models for image classification and regression
Deep learning, a branch of machine learning, has been gaining ground in many research fields as well as practical applications. Such ongoing boom can be traced back mainly to the availability and the affordability of potential processing facilities, which were not widely accessible than just a …
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Systematic Modeling and Design of Sparse Deep Neural Network Accelerators
Sparse deep neural networks (DNNs) are an important computation kernel in many data and computation-intensive applications (e.g., image classification, speech recognition, and language processing). The sparsity in such kernels has motivated the development of many sparse DNN accelerators. However, …
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Automatic Concrete Defect Identification by Silencing Features of Deep Neural Network
… to aid the process of automated inspection.Deep neural networks highly suffer from the gradient vanishing problem [1]. The effect of gradient vanishing problem is very prominent on class imbalanced data-setssuch as crack detection. In this work, a deep neural architecture is proposed …
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Deep neural network acoustic models for multi-dialect Arabic speech recognition
… representing time varying signals. Artificial Neural Networks (ANNs) have also been widely used for representing time varying quasi-stationary signals. Arabic is one of the oldest living languages and one of the oldest Semitic languages in the world, it is also the fifth most generally used …
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Exploring the landscape of backdoor attacks on deep neural network models
Deep neural networks have recently been demonstrated to be vulnerable to backdoor attacks. Specifically, by introducing a small set of training inputs, an adversary is able to plant a backdoor in the trained model that enables them to fully control the model's behavior during inference. In this …
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X-ray CT scatter correction by a physics-motivated deep neural network
… In this thesis, two novel physics-motivated deep-learning-based methods are proposed. The methods estimate and correct for the scatter in the obtained projection measurements. They incorporate both an initial reconstruction of the object of interest and the scatter-corrupted measurements …
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An entropy-based approach to network attack classification with deep neural network
Detecting and classifying attacks on computer networks is a significant challenge for network providers and users. This thesis project builds a deep neural network to detect and classify network attacks. Our approach based on the hypothesis that each type of network attacks generates a …
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Accelerating Distributed Deep Neural Network Training and Fine-Tuning Through Resource Interleaving
… increase in dataset and model sizes of deep learning has created a massive demand for efficient GPU clusters. As the number of GPUs increases, the communication overhead of distributed Machine Learning (ML) training and fine-tuning workloads quickly takes up a significant portion of …
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Architecture design for highly flexible and energy-efficient deep neural network accelerators
Deep neural networks (DNNs) are the backbone of modern artificial intelligence (AI). However, due to their high computational complexity and diverse shapes and sizes, dedicated accelerators that can achieve high performance and energy efficiency across a wide range of DNNs are critical for enabling …
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Toward Predictable and Efficient Deep Neural Network Inference on Graphics Processing Units
GPUs dominate DNN inference but remain difficult to control predictably under multi-tenant load. This thesis presents a practical, end-to-end approach for predictable, efficient single-GPU inference built around a closed loop of predict → allocate → power-tune. First, we introduce SGPRS, a …
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Texture-based Deep Neural Network for Histopathology Cancer Whole Slide Image (WSI) Classification
… WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The …
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Evidential Deep Learning for uncertainty quantification in jet tagging deep neural network model
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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Analysis of a deep neural network for missing transverse momentum reconstruction in ATLAS
… in the transverse plane. In this work, a deep neural network was trained using supervised learning to measure this imbalance. The performance of this network was evaluated in MC simulation and in 43 fb⁻¹ of data recorded at ATLAS. The network offered superior resolution and significantly …
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