Search Results for author:
Found 6 papers, 3 papers with code
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets
This paper aims to fill this gap by examining the specific challenges posed by data imbalance in self-supervised learning in the domain of tabular data, with a primary focus on autoencoders.
Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory
In this paper, we propose a data augmentation procedure, the GOLIATH algorithm, based on kernel density estimates which can be used in classification and regression.
Data Augmentation for Imbalanced Regression
In this work, we consider the problem of imbalanced data in a regression framework when the imbalanced phenomenon concerns continuous or discrete covariates.
Non-parametric Clustering of Multivariate Populations with Arbitrary Sizes
It relies on the differences between orthogonal projection coefficients of the K density copulas estimated from the K populations.
Automatic recognition of flow cytometric phytoplankton functional groups using convolutional neural networks
In this study, we present a convolutional neural network (CNN) to classify several PFGs using AFCM pulse shapes.
Mixed data Deep Gaussian Mixture Model: A clustering model for mixed datasets
In this work we introduce a multilayer architecture model-based clustering method called Mixed Deep Gaussian Mixture Model (MDGMM) that can be viewed as an automatic way to merge the clustering performed separately on continuous and non-continuous data.
