219 citations · 734 across the 29 of their papers we have counts for
9 papers · 1 filter
Learning Summary Statistics for Bayesian Inference with Autoencoders
Carlo Albert, Simone Ulzega, Firat Ozdemir +2
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of obser…
Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network
Michael Stalder, Firat Ozdemir, Artur Safin +3
Accurate lake temperature estimation is essential for numerous problems tackled in both hydrological and ecological domains. Nowadays physical models are developed to estimate lake…
Probabilistic Time of Arrival Localization
Fernando Perez-Cruz, Pablo M. Olmos, Michael Minyi Zhang +1
In this paper, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that…
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
Stefan G. Stark, Stephanie L. Hyland, Melanie F. Pradier +5
The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into…
Out-of-Sample Testing for GANs
Pablo Sánchez-Martín, Pablo M. Olmos, Fernando Pérez-Cruz
We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a test set to directly measure the reconstruction quality in the original sample space (no auxiliary net…
Sparse Three-parameter Restricted Indian Buffet Process for Understanding International Trade
Melanie F. Pradier, Viktor Stojkoski, Zoran Utkovski +2
This paper presents a Bayesian nonparametric latent feature model specially suitable for exploratory analysis of high-dimensional count data. We perform a non-negative doubly spars…
Complex-Valued Gaussian Processes for Regression
Rafael Boloix-Tortosa, Eva Arias-de-Reyna, F. Javier Payan-Somet +1
In this paper we propose a novel Bayesian solution for nonlinear regression in complex fields. Previous solutions for kernels methods usually assume a complexification approach, wh…
Deep Learning for Multi-label Classification
Jesse Read, Fernando Perez-Cruz
In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time.…
Gaussian Processes for Nonlinear Signal Processing
Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes +2
Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal pro…