
Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations
In this paper we introduce the temporally factorized 3D convolution (3TC...
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GAITprop: A biologically plausible learning rule derived from backpropagation of error
Traditional backpropagation of error, though a highly successful algorit...
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SpikeTimingDependent Inference of Synaptic Weights
A potential solution to the weight transport problem, which questions th...
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Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks
Perceived personality traits attributed to an individual do not have to ...
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Temporal Factorization of 3D Convolutional Kernels
3D convolutional neural networks are difficult to train because they are...
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Causal inference using Bayesian nonparametric quasiexperimental design
The de facto standard for causal inference is the randomized controlled ...
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Modulation of early visual processing alleviates capacity limits in solving multiple tasks
In daily life situations, we have to perform multiple tasks given a visu...
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Perturbative estimation of stochastic gradients
In this paper we introduce a family of stochastic gradient estimation te...
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Forward Amortized Inference for LikelihoodFree Variational Marginalization
In this paper, we introduce a new form of amortized variational inferenc...
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Wasserstein Variational Inference
This paper introduces Wasserstein variational inference, a new form of a...
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First Impressions: A Survey on Computer VisionBased Apparent Personality Trait Analysis
Personality analysis has been widely studied in psychology, neuropsychol...
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Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Explainability and interpretability are two critical aspects of decision...
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The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables
This paper introduces the kernel mixture network, a new method for nonpa...
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Endtoend semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks
Recent years have seen a sharp increase in the number of related yet dis...
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Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition
Here, we develop an audiovisual deep residual network for multimodal app...
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Convolutional Sketch Inversion
In this paper, we use deep neural networks for inverting face sketches t...
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Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dyn...
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Regularizing Solutions to the MEG Inverse Problem Using SpaceTime Separable Covariance Functions
In magnetoencephalography (MEG) the conventional approach to source reco...
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Marcel A. J. van Gerven
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Understanding how mind emerges from matter is one of the great remaining questions in science. The Artificial Cognitive Systems lab studies the computational principles that underly natural intelligence and uses these principles in the development of generalpurpose intelligent machines.