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Found 6 papers, 4 papers with code
Pose-based Tremor Type and Level Analysis for Parkinson's Disease from Video
The accuracy of diagnosis ranges between 73% and 84%, and is influenced by the experience of the clinical assessor.
Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI
To overcome that, we propose a graph learning framework that captures comprehensive features by integrating both correlation and distance-based similarity measures under a contrastive loss.
Towards Graph Representation Learning Based Surgical Workflow Anticipation
Hence, we propose a graph representation learning framework to comprehensively represent instrument motions in the surgical workflow anticipation problem.
Pose-based Tremor Classification for Parkinson's Disease Diagnosis from Video
To this end, we propose to classify Parkinson's tremor since it is one of the most predominant symptoms of PD with strong generalizability.
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network
For high-dimensional data, there are huge communication costs for distributed GBDT because the communication volume of GBDT is related to the number of features.
Greedy Step Averaging: A parameter-free stochastic optimization method
In this paper we present the greedy step averaging(GSA) method, a parameter-free stochastic optimization algorithm for a variety of machine learning problems.
