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Showing 1 to 3 of 3 for “"Neural 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
… exploring 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 …