1.1k citations · 1.5k across the 21 of their papers we have counts for
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Learning Functional Causal Models with Generative Neural Networks
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou +3
We introduce a new approach to functional causal modeling from observational data, called Causal Generative Neural Networks (CGNN). CGNN leverages the power of neural networks to l…
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…
Estimating Nonlinear Dynamics with the ConvNet Smoother
Luca Ambrogioni, Umut Güçlü, Eric Maris +1
Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering. The most well-known method, the Ka…