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 9 of 9 for “"Neural Architecture Search (NAS)"”.
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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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Towards searching for the best student in a Knowledge Distillation framework
… of identifying an optimal student—balancing architecture and training hyperparameters—is often hindered by the extensive and computationally intensive search required. This thesis introduces the KD-Policy-Learning (KD-PL) framework, a novel approach designed to mitigate this challenge. KD-PL …
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On Impact of Network Architecture for Deep Learning
<p>The architecture of neural networks is a crucial factor in the success of deep learning models across a range of fields, including computer vision and natural language processing (NLP). Specific architectures are tailored to address particular tasks, and the selection of architecture can …
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Searching for Efficient Multi-Stage Vision Transformers
… in comparable performance to convolutional neural networks (CNN), which have been studied in computer vision for years. This naturally raises the question of how the performance of ViT can be advanced with design techniques of CNN. To this end, we propose to incorporate two techniques and …
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Learning Neural Network Architecture from Data: NAS and Dynamic Networks
A myriad of breakthroughs in neural network architecture has brought significant improvement on wide range of deep learning tasks. Despite the large advances brought about by network design, manually finding well-optimized network architecture is challenging given large amount of design choices. …
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Evolutionary Optimization of Neural Architectures for Remaining Useful Life Prediction
… approaches, in particular employing deep neural networks (DNNs), have shown success in the RUL prediction task. However, although their architecture considerably affects performance, DNNs are usually handcrafted by human experts via a labor-intensive design process. To overcome this issue, …
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Efficient Deep Learning: Model Design and Algorithmic Innovation
… training, and deployment. Central to this research is the development of advanced Neural Architecture Search (NAS) frameworks. These frameworks enable the automated design of lightweight, task-specific neural networks optimized for resource-constrained scenarios, such as mobile devices and …
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Designing Highly-Efficient Hardware Accelerators for Robust and Automatic Deep Learning Technologies
… AI technologies, such as deep convolutional neural networks (DNNs), have recently achieved amazing success in numerous applications, such as image recognition, autonomous driving, and so on. However, there are two critical issues in the conventional DNN applications. The first problem is …
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Taming TinyML: deep learning inference at computational extremes
The advanced data modelling capabilities of neural networks allowed deep learning to become a cornerstone of many applications of artificial intelligence (AI). AI can be brought into our environments by deploying neural models to ubiquitous Internet-of-Things (IoT), wearable and embedded devices to …