Search Results for author:
Found 2 papers, 1 papers with code
Learning Activation Functions for Sparse Neural Networks
By conducting experiments on popular DNN models (LeNet-5, VGG-16, ResNet-18, and EfficientNet-B0) trained on MNIST, CIFAR-10, and ImageNet-16 datasets, we show that the novel combination of these two approaches, dubbed Sparse Activation Function Search, short: SAFS, results in up to 15. 53%, 8. 88%, and 6. 33% absolute improvement in the accuracy for LeNet-5, VGG-16, and ResNet-18 over the default training protocols, especially at high pruning ratios.
Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search
This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF), a pathological characteristic of Electrocardiogram (ECG) that can lead to fatal conditions such as heart attack.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
