IEEE

Comparison of Deep-Learning Models for Classification of Cellular Phenotype From Flow Cytometry Data

Abstract: This study compares the relative utility of deep learning models as automated phenotypic classifiers, built with features of peripheral blood cell populations assayed with flow cytometry. We ...
IEEE

Multiclass Generalized Labeled Multi-Bernoulli Filter With Kernel Density Estimation for Target Tracking in Unknown Backgrounds

Abstract: In multitarget tracking (MTT), the prior information of background parameters, such as clutter intensity and detection probability, exerts a significant influence on the performance of the ...
GitHub

Dense_NN is an EigenNET project for training dense-type neural networks implemented in C++.

Dense network architecture with adjustable depth and activation functions Iterative training, i.e., retraining the deep learning structure, is possible. Eigen-based matrix operations for maximum speed ...
GitHub

AKDE: Multivariate Adaptive Kernel Density Estimation Using Gaussian Mixture Model

AKDE provides an accurate, adaptive kernel density estimator based on the Gaussian Mixture Model for multidimensional data. This Python implementation includes automatic grid construction for ...