45 citations · 119 across the 13 of their papers we have counts for
14 papers
Conditional Diffusion Sampling
Francisco M. Castro-Macías, Pablo Morales-Álvarez, Saifuddin Syed +3
Sampling from unnormalized multimodal distributions with limited density evaluations remains a fundamental challenge in machine learning and natural sciences. Successful approaches…
Improving the Linearized Laplace Approximation via Quadratic Approximations
Pedro Jiménez, Luis A. Ortega, Pablo Morales-Álvarez +1
Deep neural networks (DNNs) often produce overconfident out-of-distribution predictions, motivating Bayesian uncertainty quantification. The Linearized Laplace Approximation (LLA)…
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…
Sm: enhanced localization in Multiple Instance Learning for medical imaging classification
Francisco M. Castro-Macías, Pablo Morales-Álvarez, Yunan Wu +2
Multiple Instance Learning (MIL) is widely used in medical imaging classification to reduce the labeling effort. While only bag labels are available for training, one typically see…
Focused Active Learning for Histopathological Image Classification
Arne Schmidt, Pablo Morales-Álvarez, Lee A. D. Cooper +4
Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing A…
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…
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images
Pablo Morales-Álvarez, Arne Schmidt, José Miguel Hernández-Lobato +1
In the last years, the weakly supervised paradigm of multiple instance learning (MIL) has become very popular in many different areas. A paradigmatic example is computational patho…
Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation
Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina
Medical image segmentation is a challenging task, particularly due to inter- and intra-observer variability, even between medical experts. In this paper, we propose a novel model,…
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection
Yunan Wu, Francisco M. Castro-Macías, Pablo Morales-Álvarez +2
Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown. Traditional MIL assum…
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…
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion
Daniel Heestermans Svendsen, Pablo Morales-Alvarez, Ana Belen Ruescas +2
Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radi…
Deep Gaussian Processes for geophysical parameter retrieval
Daniel Heestermans Svendsen, Pablo Morales-Álvarez, Rafael Molina +1
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical…
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…