2.7k citations · 2.7k across the 4 of their papers we have counts for
4 papers
An Analysis of Regularization and Fokker-Planck Residuals in Diffusion Models for Image Generation
Onno Niemann, Gonzalo Martínez Muñoz, Alberto Suárez Gonzalez
Recent work has shown that diffusion models trained with the denoising score matching (DSM) objective often violate the Fokker--Planck (FP) equation that governs the evolution of t…
A Comparative Analysis of XGBoost
Candice Bentéjac, Anna Csörgő, Gonzalo Martínez-Muñoz
XGBoost is a scalable ensemble technique based on gradient boosting that has demonstrated to be a reliable and efficient machine learning challenge solver. This work proposes a pra…
Pooling homogeneous ensembles to build heterogeneous ones
Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez
In ensemble methods, the outputs of a collection of diverse classifiers are combined in the expectation that the global prediction be more accurate than the individual ones. Hetero…
Vote-boosting ensembles
Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier,…