30 citations · 72 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 17 cited
A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks
Seyedsaman Emami, Gonzalo Martínez-Muñoz
Deep learning has revolutionized the computer vision and image classification domains. In this context Convolutional Neural Networks (CNNs) based architectures are the most widely…
cs.LG2022★ 30 cited
Condensed Gradient Boosting
Seyedsaman Emami, Gonzalo Martínez-Muñoz
This paper presents a computationally efficient variant of gradient boosting for multi-class classification and multi-output regression tasks. Standard gradient boosting uses a 1-v…
astro-ph.CO2022★ 25 cited
Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
Daniel de Andres, Gustavo Yepes, Federico Sembolini +5
In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (st…