Tools for assessing clustering robustness
Exploring non-gradient-based learning techniques for training neural networks, using brute force parameter search and optimization methods. Includes comparison with gradient-based learning.
This repository contains the modules implementing a Machine Learning-based solution for optimizing the execution of dislib algorithms. In particular, a stacked classification model is leveraged to predict the most suitable value of the block-size parameter for the execution of dislib algorithms.
Advances in the models for studying cardiovascular physiology: Using parameter sensitivity analysis (PSA) to reduce the length of the voltage protocol
Optimization of parameter values means finding the best combination of the parameters that governs the model, to enable it to perform the given task with relative accuracy
Unscented Kalman Filter (UKF), to enhance modeling and understanding of neural dynamics from fMRI data using Coupled Oscillator Model.
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