33 citations · 86 across the 5 of their papers we have counts for
5 papers
Robust multi-task boosting using clustering and local ensembling
Seyedsaman Emami, Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz
Multi-Task Learning (MTL) aims to boost predictive performance by sharing information across related tasks, yet conventional methods often suffer from negative transfer when unrela…
Robust-Multi-Task Gradient Boosting
Seyedsaman Emami, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato
Multi-task learning (MTL) has shown effectiveness in exploiting shared information across tasks to improve generalization. MTL assumes tasks share similarities that can improve per…
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…
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…
Sequential Training of Neural Networks with Gradient Boosting
Seyedsaman Emami, Gonzalo Martínez-Muñoz
This paper presents a novel technique based on gradient boosting to train the final layers of a neural network (NN). Gradient boosting is an additive expansion algorithm in which a…