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Found 7 papers, 2 papers with code
Community Detection with Known, Unknown, or Partially Known Auxiliary Latent Variables
We analyze the conditions for exact recovery when these auxiliary latent variables are unknown, representing unknown nuisance parameters or model mismatch.
Real-Time EMG Signal Classification via Recurrent Neural Networks
Real-time classification of Electromyography signals is the most challenging part of controlling a prosthetic hand.
EMG-Based Feature Extraction and Classification for Prosthetic Hand Control
The experimental results illustrate that the proposed method enhances the accuracy of real-time classification of EMG signals up to $95. 5\%$ for $800$ msec signal length.
EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine
Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with high accuracy is available.
Semidefinite Programming for Community Detection with Side Information
SDP is an efficient solution for standard community detection on graphs.
Community Detection: Exact Recovery in Weighted Graphs
In community detection, the exact recovery of communities (clusters) has been mainly investigated under the general stochastic block model with edges drawn from Bernoulli distributions.
Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information
Another contribution of this paper is relevant to non-graph observations (independent side information) that exists beside a graph realization in many applications.
