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 65 for “"neural architecture"”.
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NEURAL ARCHITECTURE DESIGN AND APPLICATIONS
Neural architecture design is crucial in AI development. This thesis first examines the macroscopic architecture of Transformers, challenging the belief that their attention-based token mixer is key. By replacing the attention module with a simple spatial pooling operator, we create PoolFormer, …
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Computational model for neural architecture search
… 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 effort as they are either …
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Optimization and automation for efficient neural architecture design
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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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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Hyperparameters and neural architectures in differentially private deep learning
… impact of changes to the hyperparameters and the neural architecture on the utility/privacy tradeoff, the main tradeoff in DP, for models trained on the MIMIC-III dataset. The analyzed hyperparameters are the noise multiplier, clipping bound, and batch size. The experiments examine neural …
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A new class of neural architectures to model episodic memory : computational studies of distal reward learning
… based on the mammalian brain. A computational neural architecture instantiates the proposed model and is tested on a particular task of distal reward learning. Categorical Neural Semantic Theory informs the architecture design. To experiment upon the computational brain model, embodiment and an …
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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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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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A Temporal Fuzzy-ART Neural Network Architecture as a Model of Phoneme Perception
… of Adaptive Resistance Theory- (ART-) type neural networks for finding and encoding linguistic structures, specifically those corresponding to acoustic patterns in natural speech. We build an interpretation of human perceptual response to acoustic pattern in natural speech, translating this …
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Emotion Regulation Through Distancing: Developing a Novel Neurocognitive Model
… a preliminary neurocognitive model. I tested the neural architecture of this model through a meta-analysis of fMRI literature, and then further validated and refined it by comparing three forms of distancing in an fMRI study. Finally, I investigated self-projection and its relation to the left …
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Demystifying deep network architectures : from theory to applications
Deep neural networks significantly power the success of machine learning and artificial intelligence. Over the past decade, the community keeps designing architectures of deep layers and complicated connections. Many works in deep learning theory tried to understand deep networks from different …
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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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Efficient Algorithms and Systems for Tiny Deep Learning
… a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers. TinyNAS adopts a two-stage neural architecture search approach that first optimizes the search space to fit the …
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Automated and Handcrafted Neural Network Design for Vision Applications
This thesis presents effective neural network design for various vision applications from two aspects, automated neural network design and handcrafted neural network design. To be more specific, in the first step, it applies novel neural architecture search algorithms on single image deraining and …
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Efficient Multi-Objective NeuroEvolution in Computer Vision and Applications for Threat Identification
… effective multi-objective AutoML strategies for neural architecture search. These strategies are designed for threat detection and provide in- sights into some quintessential computer vision problems. To this end, the thesis first introduces two new models, a practical Multi-Objective …
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Multilingual multitask joint neural information extraction
… information extraction tasks and build a joint neural architecture that performs multiple IE tasks within a single model. We first focus on the generality of IE models. As most existing neural models use word embeddings as input features, they are sensitive to the quality of word …
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Utilizing the Transformer Architecture for Question Answering
… we utilize the transformer, a state-of-the-art neural architecture to study two QA problems: the answer sentence selection and the answer summary generation. For answer sentence selection, we present two new approaches that rank a list of candidate answers for a given question by utilizing …
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Neural Voice Activity Detection and its practical use
… 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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Neural Sequences Underlying Directed Turning in C. elegans
… not well understood. Here, we characterize the architecture of neural circuits that control C. elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These …
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