219 citations · 734 across the 29 of their papers we have counts for
8 papers · 1 filter
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation
Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz
Neural likelihood estimation methods for simulation-based inference can suffer from performance degradation when the modeled data is very high-dimensional or lies along a lower-dim…
Adaptive Annealed Importance Sampling with Constant Rate Progress
Shirin Goshtasbpour, Victor Cohen, Fernando Perez-Cruz
Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution given its unnormalized density function. This algorithm relies on a sequence of int…
Optimization of Annealed Importance Sampling Hyperparameters
Shirin Goshtasbpour, Fernando Perez-Cruz
Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unb…
Improved BiGAN training with marginal likelihood equalization
Pablo Sánchez-Martín, Pablo M. Olmos, Fernando Perez-Cruz
We propose a novel training procedure for improving the performance of generative adversarial networks (GANs), especially to bidirectional GANs. First, we enforce that the empirica…
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models
Michael Minyi Zhang, Sinead A. Williamson, Fernando Perez-Cruz
Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing feat…
Complex-Valued Kernel Methods for Regression
Rafael Boloix-Tortosa, Juan José Murillo-Fuentes, Irene Santos Velázquez +1
Usually, complex-valued RKHS are presented as an straightforward application of the real-valued case. In this paper we prove that this procedure yields a limited solution for regre…
Bayesian Nonparametric Crowdsourcing
Pablo G. Moreno, Yee Whye Teh, Fernando Perez-Cruz +1
Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce r…
Bayesian nonparametric comorbidity analysis of psychiatric disorders
Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco +1
The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psy…