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Found 3 papers, 1 papers with code
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis
We present NERULA (Non-contrastive ECG and Reconstruction Unsupervised Learning Algorithm), a self-supervised framework designed for single-lead ECG signals.
Reading Between the Leads: Local Lead-Attention Based Classification of Electrocardiogram Signals
Self-attention models have emerged as powerful tools in both computer vision and Natural Language Processing (NLP) domains.
Ranked #1 on
ECG Classification
on PhysioNet Challenge 2021
Focal-WNet: An Architecture Unifying Convolution and Attention for Depth Estimation
Along with the focal transformer layers, we leverage a convolutional architecture to learn depth cues that cannot be learned by a transformer alone as some cues like occlusion require a local receptive field and are easier for a conv-net to learn.
Ranked #40 on
Monocular Depth Estimation
on KITTI Eigen split
