IEEE

EM-Based Hyperparameter Optimization for Regularized Volterra Kernel Estimation

Abstract: In nonlinear system identification, Volterra kernel estimation based on regularized least squares can be performed by taking a Bayesian approach. In this framework, covariance structures ...
IEEE

Kernel Recursive Ridge Regression With Varying Hyperparameter and Favorable Sample Incorporation for Slow Time-Varying Process Monitoring

Abstract: Industrial process data typical exhibit nonlinear and time-varying characteristics, which limit the effectiveness of traditional process monitoring methods. Kernel-based techniques can ...
GitHub

CPA - Compositional Perturbation Autoencoder

CPA is a framework to learn the effects of perturbations at the single-cell level. CPA encodes and learns phenotypic drug responses across different cell types, doses, and combinations. CPA allows: ...
Frontiers

Convergence analysis of hyperparameter-free MCC-based channel estimation for mmWave MIMO systems

Accurate channel-estimation algorithms are critical for enhancing the throughput of wireless communication systems, including millimetre wave (mmWave) multiple-input multiple-output (MIMO) systems, ...
GitHub

ESCHR: A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets

This example jupyter notebook on Google Colab provides a walkthrough of ESCHR analysis using an example scRNA-seq dataset. If you launch the notebook in Google Colab ...
Europe PMC

On a framework of data assimilation for hyperparameter estimation of spiking neuronal networks.

When handling real-world data modeled by a complex network dynamical system, the number of the parameters is often much more than the size of the data. Therefore, in many cases, it is impossible to ...