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 “"Network pruning"”.
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Neural Network Pruning for ECG Arrhythmia Classification
<p>Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made …
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Fine Granularity is Critical for Intelligent Neural Network Pruning
Neural network pruning is a popular approach to reducing the computational costs of training and/or deploying a network, and aims to do so while minimizing accuracy loss. Pruning methods that remove individual weights (fine granularity) yield better ratios of accuracy to parameter count, while …
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Achieving More with Less: Learning Generalizable Neural Networks With Less Labeled Data and Computational Overheads
… different ways to learn generalizable neural networks that require less labeled data and computational resources. We demonstrate that using physics supervision in scientific problems can reduce the need for labeled data, thereby improving data efficiency without compromising model …
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Deep Learning for online tagging of proton-proton collisions at the High-Luminosity LHC
… for enhancing the trigger process. Deep Neural Networks can identify patterns in large datasets efficiently, making them suitable for early-stage data selection. In particular, Deep Neural Networks can be implemented on FPGAs used in trigger boards, offering the necessary speed and flexibility …
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Pruning Convolution Neural Network (SqueezeNet) for Efficient Hardware Deployment
… the model size of the Convolution Neural Network (CNN) by various compression techniques like Architectural compression, Pruning, Quantization, and Encoding (e.g., Huffman encoding). Network pruning is one of the promising technique to solve these problems. This thesis proposes methods to …
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Learning and adapting visual models for multiple specialized tasks
… learning of multiple tasks with separate deep networks, such as the work described above, is the need to store separate models, which increases storage requirements and affects scalability. We formulate and present two novel methods that draw inspiration from network pruning and weight …
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Taming TinyML: deep learning inference at computational extremes
… 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 …
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Parameter reduction in deep learning and classification
… generalize well, while in deep learning, neural networks have shown to achieve state-of-the-art results, especially in the area of image recognition, in their current state cannot be easily deployed on memory restricted Internet-of-Things devices. Although much work has been carried out on …