18 citations · 40 across the 4 of their papers we have counts for
6 papers
k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
Luca Ambrogioni, Umut Güçlü, Marcel van Gerven
Generative adversarial networks (GANs) are the state of the art in generative modeling. Unfortunately, most GAN methods are susceptible to mode collapse, meaning that they tend to…
Deep adversarial neural decoding
Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger +3
Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approac…
The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables
Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven +1
This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex…
End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks
Umut Güçlü, Yağmur Güçlütürk, Meysam Madadi +5
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths…
Brains on Beats
Umut Güçlü, Jordy Thielen, Michael Hanke +1
We developed task-optimized deep neural networks (DNNs) that achieved state-of-the-art performance in different evaluation scenarios for automatic music tagging. These DNNs were su…
Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions
Arno Solin, Pasi Jylänki, Jaakko Kauramäki +3
In magnetoencephalography (MEG) the conventional approach to source reconstruction is to solve the underdetermined inverse problem independently over time and space. Here we presen…