1 code implementation • 11 Nov 2022 • Burcu Küçükoğlu, Walraaf Borkent, Bodo Rueckauer, Nasir Ahmad, Umut Güçlü, Marcel van Gerven
Predictive processing is a popular theoretical framework which maintains that the human brain is actively seeking to minimize surprise.
no code implementations • 29 Sep 2021 • Burcu Küçükoğlu, Walraaf Borkent, Bodo Rueckauer, Nasir Ahmad, Umut Güçlü, Marcel van Gerven
Predictive processing is a popular theoretical framework which maintains that the human brain is actively seeking to minimize surprise.
no code implementations • 1 Jan 2021 • Thirza Dado, Yağmur Güçlütürk, Luca Ambrogioni, Gabrielle Ras, Sander E. Bosch, Marcel van Gerven, Umut Güçlü
We introduce a new framework for hyperrealistic reconstruction of perceived naturalistic stimuli from brain recordings.
no code implementations • 1 Jan 2021 • Gabrielle Ras, Luca Ambrogioni, Pim Haselager, Marcel van Gerven, Umut Güçlü
In a 3TConv the 3D convolutional filter is obtained by learning a 2D filter and a set of temporal transformation parameters, resulting in a sparse filter requiring less parameters.
no code implementations • 29 Jun 2020 • Gabriëlle Ras, Luca Ambrogioni, Pim Haselager, Marcel A. J. van Gerven, Umut Güçlü
Finally, we implicitly demonstrate that, in popular ConvNets, the 2DConv can be replaced with a 3TConv and that the weights can be transferred to yield pretrained 3TConvs.
no code implementations • 29 Jan 2020 • Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Brahim Chaib-Draa, Marcel van Gerven, Francois Laviolette
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes.
no code implementations • 20 Dec 2019 • Gabriëlle Ras, Ron Dotsch, Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven
It is important that we understand the driving factors behind the predictions, in humans and in deep neural networks.
no code implementations • 9 Dec 2019 • Gabriëlle Ras, Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven
3D convolutional neural networks are difficult to train because they are parameter-expensive and data-hungry.
no code implementations • 9 Jul 2019 • Luca Ambrogioni, Umut Güçlü, Marcel van Gerven
A possible way of dealing with this problem is to use an ensemble of GANs, where (ideally) each network models a single mode.
no code implementations • NeurIPS 2018 • Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory.
no code implementations • 29 May 2018 • Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss.
no code implementations • 21 Apr 2018 • Julio C. S. Jacques Junior, Yağmur Güçlütürk, Marc Pérez, Umut Güçlü, Carlos Andujar, Xavier Baró, Hugo Jair Escalante, Isabelle Guyon, Marcel A. J. van Gerven, Rob Van Lier, Sergio Escalera
However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data.
no code implementations • NeurIPS 2017 • Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob Van Lier, Marcel A. J. van Gerven
Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning.
1 code implementation • 19 May 2017 • Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven, Eric Maris
In the Bayesian filtering example, we show that the method can be used to filter complex nonlinear and non-Gaussian signals defined on manifolds.
1 code implementation • 19 May 2017 • Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob Van Lier, Marcel van Gerven
Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning.
no code implementations • 9 Mar 2017 • Umut Güçlü, Yağmur Güçlütürk, Meysam Madadi, Sergio Escalera, Xavier Baró, Jordi González, Rob Van Lier, Marcel A. J. van Gerven
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation.
no code implementations • 17 Feb 2017 • Luca Ambrogioni, Umut Güçlü, Eric Maris, Marcel van Gerven
Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering.
1 code implementation • 16 Sep 2016 • Yağmur Güçlütürk, Umut Güçlü, Marcel A. J. van Gerven, Rob Van Lier
Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition.
2 code implementations • 9 Jun 2016 • Yağmur Güçlütürk, Umut Güçlü, Rob Van Lier, Marcel A. J. van Gerven
In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images.
no code implementations • 9 Jun 2016 • Umut Güçlü, Marcel A. J. van Gerven
Encoding models are used for predicting brain activity in response to sensory stimuli with the objective of elucidating how sensory information is represented in the brain.
Neurons and Cognition
1 code implementation • NeurIPS 2016 • Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel A. J. van Gerven
We developed task-optimized deep neural networks (DNNs) that achieved state-of-the-art performance in different evaluation scenarios for automatic music tagging.
Neurons and Cognition
no code implementations • 24 Nov 2014 • Umut Güçlü, Marcel A. J. van Gerven
Converging evidence suggests that the mammalian ventral visual pathway encodes increasingly complex stimulus features in downstream areas.