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 90 for “"Deep Neural Networks (DNNs)"”.
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Synthetic Data Generation and Sampling for Online Training of DNN in Manufacturing Supervised Learning Problems
… passive data from manufacturing systems and networks, which enables data-driven modeling with high-data-demand, advanced statistical models such as Deep Neural Networks (DNNs). Deep Neural Networks (DNNs) have proven to be remarkably effective in supervised learning in critical manufacturing …
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Deep neural networks for choice analysis
As deep neural networks (DNNs) outperform classical discrete choice models (DCMs) in many empirical studies, one pressing question is how to reconcile them in the context of choice analysis. So far researchers mainly compare their prediction accuracy, treating them as completely different modeling …
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Towards Understanding Human-aligned Neural Representation in the Presence of Confounding Variables
Deep Neural Networks (DNNs) find one out of many possible solutions to a given task such as classification. This solution is more likely to pick up on spurious features and low-level statistical patterns in the train data rather than semantic features and highlevel abstractions, resulting in poor …
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Provably reliable machine learning systems
Machine learning systems, which primarily use deep neural networks (DNNs), serve as critical components in safety-critical applications and compound AI systems. Despite their ubiquity, automated formal reasoning about their reliability has lagged significantly. Neural network verification is …
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De-noising and de-blurring of images using deep neural networks
Deep Neural Networks (DNNs) [1] are often used for image reconstruction, but perform better reconstructing the low frequencies of the image than the high frequencies. This is especially the case when using noisy images. In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) …
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Large-scale training of deep neural networks
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with growing datasets, reduce training times, and enable training on memory-constrained problems where parallelism is necessary. In this thesis, I present a set of techniques that can leverage large …
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Efficient inference of convolutional neural networks on general purpose hardware using weight repetition
Deep Neural Networks (DNNs) have begun to permeate all corners of electronic society due to their high accuracy and machine efficiency per operation. Recent work has shown how weights within and across DNN filters have large degrees of repetition due to the pigeonhole principle and modern weight …
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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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Neural Voice Activity Detection and its practical use
… recently, however, statistical models, including Deep Neural Networks (DNNs) have been explored. In this thesis, I explore the use of a lightweight, deep, recurrent neural architecture for VAD. I also explore a variant that is fully end-to-end, learning features directly from raw waveform data. In …
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Trustworthy and Efficient Knowledge Sharing across Tasks in Deep Neural Networks
Deep neural networks (DNNs) have revolutionized various fields with their superior performance on particular tasks. However, these tasks in real-world applications are often interrelated, raising the necessity for DNNs to share and transfer knowledge reliably and effectively between tasks. To solve …
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Towards Better Representations with Deep/Bayesian Learning
<p>Deep learning and Bayesian Learning are two popular research topics in machine learning. They provide the flexible representations in the complementary manner. Therefore, it is desirable to take the best from both fields. This thesis focuses on the intersection of the two topics— enriching one …
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Generalization of deep neural networks to unseen attribute combinations
… and shape before. However, is it the case that deep neural networks (DNNs) are able to generalize to such novel combinations in object recognition or other related vision tasks? This thesis demonstrates that (1) the ability of DNNs to generalize to unseen attribute combinations increases with …
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MODEL ADAPTATION FOR EDGE AI
Deployment of Deep Neural Networks (DNNs) on edge devices presents significant challenges due to their computational demands. Existing model compression techniques often fall short by being oblivious to downstream user-specific tasks. This thesis addresses the challenge of adapting DNN models …
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The Effect Of Hyperparameters In The Activation Layers Of Deep Neural Networks
Deep neural networks (DNNs), and artificial neural networks (ANNs) in general, have recently received a great amount of attention from both the media and the machine learning community at large. DNNs have been used to produce world-class results in a variety of domains, including image recognition, …
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Mixed-precision NN accelerator with neural-hardware architecture search
Neural architecture and hardware architecture co-design is an effective way to enable specialization and acceleration for deep neural networks (DNNs). The design space and its exploration methodology impact efficiency and productivity. However, both architecture designs are challenging. We first …
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ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning
Existing adversarial algorithms for Deep Reinforcement Learning (DRL) have largely focused on identifying an optimal time to attack a DRL agent. However, little work has been explored in injecting efficient adversarial perturbations in DRL environments. We propose a suite of novel DRL adversarial …
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Towards Comprehensive Visual Understanding via Deep Neural Networks
Deep neural networks (DNNs) have made significant advancements in visual scene understanding, demonstrating great potential for applications in downstream tasks such as autonomous driving, robotic navigation, and human-computer interaction. Despite these successes, generalization ability remains a …
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Neural Networks For Phase Demodulation In Optical Interferometry
Neural Networks (NNs) (or 'deep' neural networks (DNNs)) have found great success in many applications across all fields of engineering, and in particular have found recent success in the field of Photonics. In this work we discuss the application of NNs to optical interferometry for the purpose of …
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Attention mechanism in deep neural networks for computer vision tasks
… is one of the most important algorithms in the deep Learning community, was initially designed in the natural language processing for enhancing the feature representation of key sentence fragments over the context. In recent years, the attention mechanism has been widely adopted in solving …
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Annotation-Free Deep Learning of Large-Scale Nuclear Segmentation and Spatial Neighborhood Analysis on Multiplexed Fluorescence Images
Deep neural networks (DNNs) offer state-of-the-art performance for cell nucleus detection and segmentation. However, they require many manual annotations from skilled biologists for robust algorithm training, which is labor-intensive and not easily scalable. We propose an unsupervised expectation …
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