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Found 6 papers, 4 papers with code
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces
Objective: To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths.
COVID-19 detection using chest X-rays: is lung segmentation important for generalization?
Purpose: we evaluated the generalization capability of deep neural networks (DNNs), trained to classify chest X-rays as Covid-19, normal or pneumonia, using a relatively small and mixed dataset.
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification
The presented methodology surpassed performances obtained with FBCCA and SVMs (more traditional SSVEP classification methods) in BCIs with small data lengths and one electrode.
Frequency learning for image classification
Machine learning applied to computer vision and signal processing is achieving results comparable to the human brain on specific tasks due to the great improvements brought by the deep neural networks (DNN).
Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective Optimization
In dimensionality reduction problems, the adopted technique may produce disparities between the representation errors of different groups.
A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays
Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal.
