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 36 for “"Architecture Search"”.
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Computational model for neural architecture search
<p>"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable …
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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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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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Structure of Artificial Neural Networks : Empirical Investigations
… intelligence. "Deep" refers to the deep architectures with operations in manifolds of which there are no immediate observations. For these deep architectures some kind of structure is pre-defined -- but what is this structure? With a formal definition for structures of neural networks, …
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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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Towards practical neural network meta-modeling
… for convolutional neural network (CNN) architecture search. We first introduce a novel approach for CNN architecture architecture using Q-learning, a popular value iteration algorithm from the reinforcement learning community for sequential decision problems. On the task of object …
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Automated and Handcrafted Neural Network Design for Vision Applications
… in the first step, it applies novel neural architecture search algorithms on single image deraining and re-identification (reID), where unique deraining and reID search space are proposed, respectively. To step further, this thesis also introduces elaborately handcrafted networks, such as a …
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Taming TinyML: deep learning inference at computational extremes
… for deep learning, is tackled by the emerging research field called *TinyML*. In this thesis, I develop model discovery and compression methodology whose common threads are automation and holistic optimisation of network architectures and their execution software, informed by the computational …
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Efficient Multi-Objective NeuroEvolution in Computer Vision and Applications for Threat Identification
… to classic, effort-intensive hyperparameter search. However, existing approaches typ- ically show significant downsides, like their (1) high computational cost/greediness in resources, (2) limited (or absent) scalability to custom datasets, (3) inability to provide competitive alternatives to …
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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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TOWARDS EFFICIENT LARGE-SCALE BI-LEVEL OPTIMIZATION, ALGORITHMS AND APPLICATIONS
… mathematical framework with a long history of research, dealing with hierarchical optimization problems where one problem is nested within the other. Recently, with the rise of machine learning, bi-level optimization has regained attention as a theoretical framework covering a wide range of …
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Technology selection and architecture optimization of in-situ resource utilization systems
… the resources spent on exploiting individual architectures and exploring a broad selection of architectures. These include two dual-level approaches that address the discrete architecture design space differently from the continuous sizing design space and two combinatorial approaches that …
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Software and Hardware Co-design for Efficient Neural Networks
… hardware-awareness to produce efficient network architectures for emerging types of neural networks and new learning problem setups. Hardware-aware network architecture search (NAS) is able to discover more power efficient network architectures and achieve significant computational savings on …
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Design of Deep Neural Networks Formulated as Optimisation Problems
… involves the explicit definition of network architecture as well as the training of the network weights. Each process can be formulated into an optimisation algorithm and can be investigated with regard to optimisation performance. The training of the network weights is defined as a …
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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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Reliable, secure and energy-efficient AI hardware
… as explainable artificial intelligence, neural architecture search, and moving target defense to guarantee reliability, security, and energy efficiency. My Ph.D. thesis is the first effort toward developing energy, reliability, and robustness-aware AI hardware for safety-critical applications.
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Searching for Efficient Multi-Stage Vision Transformers
… present ViT-ResNAS, an efficient multi-stage ViT architecture designed with neural architecture search (NAS). First, we propose residual spatial reduction to decrease sequence lengths for deeper layers and utilize a multi-stage architecture. When reducing lengths, we add skip connections to …
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Evolutionary Optimization of Neural Architectures for Remaining Useful Life Prediction
… 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, we propose evolutionary neural architecture search (NAS) techniques that explore a search …
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Machine Learning for Physics Applications: Quantum Algorithm Design, Chaotic Dynamics, and Mass Spectrometry
… quantum algorithm design, where evolutionary search with a domain-specific language enables the synthesis of quantum algorithms that automatically scale to any problem size. By rediscovering known protocols such as the quantum Fourier transform, Grover’s search, and the Deutsch–Jozsa …
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Computer Vision with Machine Learning on Smartphones for Beauty Applications.
… network execution a highly active area of research, as evidenced by Google’s TensorFlow Lite platform and Apple’s CoreML API and Neural Engine-accelerated iOS devices. The overarching goal of the projects carried out in this thesis is to adapt existing desktop computer-oriented computer …
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