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Found 11 papers, 7 papers with code
Tiny Models are the Computational Saver for Large Models
By searching and employing the most appropriate tiny model as the computational saver for a given large model, the proposed approaches work as a novel and generic method to model compression.
Ranked #163 on
Image Classification
on ImageNet
DyCE: Dynamic Configurable Exiting for Deep Learning Compression and Scaling
Moreover, most current dynamic compression designs are monolithic and tightly integrated with base models, thereby complicating the adaptation to novel base models.
Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis
Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and natural language processing tasks.
POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective
In recent years, two competitive time series classification models, namely, ROCKET and MINIROCKET, have garnered considerable attention due to their low training cost and high accuracy.
Classification of ECG based on Hybrid Features using CNNs for Wearable Applications
To make the model immune to noise, we updated the model using frequency features and achieved good sustained performance in presence of noise with a slightly lower accuracy of 98. 69%.
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks
Deep neural networks (DNN) are a promising tool in medical applications.
A predictive analytics approach for stroke prediction using machine learning and neural networks
Therefore, it is vital to study the interdependency of these risk factors in patients' health records and understand their relative contribution to stroke prediction.
Identifying Stroke Indicators Using Rough Sets
We propose a novel rough-set based technique for ranking the importance of the various EHR records in detecting stroke.
Multistage Pruning of CNN Based ECG Classifiers for Edge Devices
However, the computational complexity of existing CNN models prohibits them from being implemented in low-powered edge devices.
SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches
A novel method for the detection of sleep apnea events (pause in breathing) from peripheral oxygen saturation (SpO2) signals obtained from wearable devices is discussed in this paper.
A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors
This paper introduces a novel method for apnea detection (pause in breathing) from electrocardiogram (ECG) signals obtained from wearable devices.
