1.1k citations · 1.5k across the 21 of their papers we have counts for
21 papers
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
Burcu Küçükoğlu, Walraaf Borkent, Bodo Rueckauer +3
Advances in reinforcement learning (RL) often rely on massive compute resources and remain notoriously sample inefficient. In contrast, the human brain is able to efficiently learn…
ChaLearn Looking at People: Inpainting and Denoising challenges
Sergio Escalera, Marti Soler, Stephane Ayache +6
Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the pro…
The Indian Chefs Process
Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier +6
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 India…
Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks
Gabriëlle Ras, Ron Dotsch, Luca Ambrogioni +2
Perceived personality traits attributed to an individual do not have to correspond to their actual personality traits and may be determined in part by the context in which one enco…
Temporal Factorization of 3D Convolutional Kernels
Gabriëlle Ras, Luca Ambrogioni, Umut Güçlü +1
3D convolutional neural networks are difficult to train because they are parameter-expensive and data-hungry. To solve these problems we propose a simple technique for learning 3D…
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…
Wasserstein variational gradient descent: From semi-discrete optimal transport to ensemble variational inference
Luca Ambrogioni, Umut Guclu, Marcel van Gerven
Particle-based variational inference offers a flexible way of approximating complex posterior distributions with a set of particles. In this paper we introduce a new particle-based…
Forward Amortized Inference for Likelihood-Free Variational Marginalization
Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya +5
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortize…
First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis
Julio C. S. Jacques Junior, Yağmur Güçlütürk, Marc Pérez +8
Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive resea…
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah +14
Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analy…
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…
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition
Yağmur Güçlütürk, Umut Güçlü, Marcel A. J. van Gerven +1
Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personali…
Convolutional Sketch Inversion
Yağmur Güçlütürk, Umut Güçlü, Rob van Lier +1
In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images. We first construct a semi-simulated dataset containing a very large…
Modeling the dynamics of human brain activity with recurrent neural networks
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. Encodin…
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…
Semantic vector space models predict neural responses to complex visual stimuli
Umut Güçlü, Marcel A. J. van Gerven
Encoding models have as their objective to predict neural responses to naturalistic stimuli with the aim of elucidating how sensory information is represented in the brain. This pr…
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway
Umut Güçlü, Marcel A. J. van Gerven
Converging evidence suggests that the mammalian ventral visual pathway encodes increasingly complex stimulus features in downstream areas. Using deep convolutional neural networks,…