Marcel van Gerven

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Radboud Universiteit

Scaling up learning with GAIT-prop

Backpropagation of error (BP) is a widely used and highly successful lea...
02/23/2021 ∙ by Sander Dalm, et al. ∙ 0 ∙ share

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Automatic variational inference with cascading flows

The automation of probabilistic reasoning is one of the primary aims of ...
02/09/2021 ∙ by Luca Ambrogioni, et al. ∙ 0 ∙ share

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A deep active inference model of the rubber-hand illusion

Understanding how perception and action deal with sensorimotor conflicts...
08/17/2020 ∙ by Thomas Rood, et al. ∙ 0 ∙ share

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Explainable Deep Learning: A Field Guide for the Uninitiated

Deep neural network (DNN) is an indispensable machine learning tool for ...
04/30/2020 ∙ by Ning Xie, et al. ∙ 23 ∙ share

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Virtual staining for mitosis detection in Breast Histopathology

We propose a virtual staining methodology based on Generative Adversaria...
03/17/2020 ∙ by Caner Mercan, et al. ∙ 0 ∙ share

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Automatic structured variational inference

The aim of probabilistic programming is to automatize every aspect of pr...
02/03/2020 ∙ by Luca Ambrogioni, et al. ∙ 0 ∙ share

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The Indian Chefs Process

This paper introduces the Indian Chefs Process (ICP), a Bayesian nonpara...
01/29/2020 ∙ by Patrick Dallaire, et al. ∙ 16 ∙ share

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k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport

Generative adversarial networks (GANs) are the state of the art in gener...
07/09/2019 ∙ by Luca Ambrogioni, et al. ∙ 0 ∙ share

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The functional role of cue-driven feature-based feedback in object recognition

Visual object recognition is not a trivial task, especially when the obj...
03/25/2019 ∙ by Sushrut Thorat, et al. ∙ 0 ∙ share

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Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference

Particle-based variational inference offers a flexible way of approximat...
11/07/2018 ∙ by Luca Ambrogioni, et al. ∙ 0 ∙ share

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Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

Issues regarding explainable AI involve four components: users, laws & r...
03/20/2018 ∙ by Gabrielle Ras, et al. ∙ 0 ∙ share

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Generalization of an Upper Bound on the Number of Nodes Needed to Achieve Linear Separability

An important issue in neural network research is how to choose the numbe...
02/10/2018 ∙ by Marjolein Troost, et al. ∙ 0 ∙ share

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Deep adversarial neural decoding

Here, we present a novel approach to solve the problem of reconstructing...
05/19/2017 ∙ by Yağmur Güçlütürk, et al. ∙ 0 ∙ share

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GP CaKe: Effective brain connectivity with causal kernels

A fundamental goal in network neuroscience is to understand how activity...
05/16/2017 ∙ by Luca Ambrogioni, et al. ∙ 0 ∙ share

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Estimating Nonlinear Dynamics with the ConvNet Smoother

Estimating the state of a dynamical system from a series of noise-corrup...
02/17/2017 ∙ by Luca Ambrogioni, et al. ∙ 0 ∙ share

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NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)

This workshop explores the interface between cognitive neuroscience and ...
01/06/2017 ∙ by Leila Wehbe, et al. ∙ 0 ∙ share

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Deep disentangled representations for volumetric reconstruction

We introduce a convolutional neural network for inferring a compact dise...
10/12/2016 ∙ by Edward Grant, et al. ∙ 0 ∙ share

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Predicting and visualizing psychological attributions with a deep neural network

Judgments about personality based on facial appearance are strong effect...
12/04/2015 ∙ by Edward Grant, et al. ∙ 0 ∙ share

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Marcel van Gerven

Featured Co-authors

Pushmeet Kohli

François Laviolette

Irina Rish

Luca Ambrogioni

Derek Doran

Philippe Giguère

Umut Güçlü

Guillermo Cecchi

Georg Langs

Moritz Grosse-Wentrup

Francesco Ciompi

Scaling up learning with GAIT-prop

Automatic variational inference with cascading flows

A deep active inference model of the rubber-hand illusion

Explainable Deep Learning: A Field Guide for the Uninitiated

Virtual staining for mitosis detection in Breast Histopathology

Automatic structured variational inference

The Indian Chefs Process

k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport

The functional role of cue-driven feature-based feedback in object recognition

Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference

Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

Generalization of an Upper Bound on the Number of Nodes Needed to Achieve Linear Separability

Deep adversarial neural decoding

GP CaKe: Effective brain connectivity with causal kernels

Estimating Nonlinear Dynamics with the ConvNet Smoother

NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)

Deep disentangled representations for volumetric reconstruction

Predicting and visualizing psychological attributions with a deep neural network