61 citations · 198 across the 8 of their papers we have counts for
12 papers
SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks
Kai Sun, Peibo Duan, Zhanglu Yan +3
On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks (ANNs), but the limited memory of these chips requires low-bit synapti…
Load Disaggregation under Incomplete Inventory with Structured Unclaimed Demand
Saman Mostafavi, Robert W. Cox
An incomplete inventory creates a specific attribution ambiguity in load disaggregation: unclaimed demand can offset changes in claimed loads without altering meter reconstruction.…
Brain Model State Space Reconstruction Using an LSTM Neural Network
Yueyang Liu, Artemio Soto-Breceda, Yun Zhao +5
Objective Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to EEG. However, this approach lacks a reliab…
Coherent False Seizure Prediction in Epilepsy, Coincidence or Providence?
Jens Müller, Hongliu Yang, Matthias Eberlein +4
Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false an…
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction
Khansa Rasheed, Junaid Qadir, Terence J. O'Brien +2
Prediction of seizure before they occur is vital for bringing normalcy to the lives of patients. Researchers employed machine learning methods using hand-crafted features for seizu…
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
Khansa Rasheed, Adnan Qayyum, Junaid Qadir +5
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practi…