45 citations · 119 across the 13 of their papers we have counts for
5 papers · 1 filter
torchmil: A PyTorch-based library for deep Multiple Instance Learning
Francisco M. Castro-Macías, Francisco J. Sáez-Maldonado, Pablo Morales-Álvarez +1
Multiple Instance Learning (MIL) is a powerful framework for weakly supervised learning, particularly useful when fine-grained annotations are unavailable. Despite growing interest…
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection
F. M. Castro-Macías, P. Morales-Álvarez, Y. Wu +2
Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been successfully applied to many different scientific areas and is particularly well suited to medical im…
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning
Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina
Multiple Instance Learning (MIL) is a weakly supervised learning paradigm that is becoming increasingly popular because it requires less labeling effort than fully supervised metho…
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Pablo Morales-Álvarez, Pablo Ruiz, Scott Coughlin +2
In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labell…
Remote Sensing Image Classification with Large Scale Gaussian Processes
Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina +1
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large…