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Automated Situated Design of Augmented Hearing Reality Algorithms

Bert de Vries, Thijs van de Laar, Hoang Minh Huu Nguyen, Wouter Nuijten, Mykola Lukashchuk
Oct 1, 2021

Automated Situated Design of Augmented Hearing Reality Algorithms

Active-Inference Audio
Bert de Vries
Bert de Vries
Professor

I am a professor at TU Eindhoven and team leader of BIASlab.

Thijs van de Laar
Thijs van de Laar
Assistant professor

I am an assistant professor at BIASlab, where I work on artificial agents that learn to control themselves in uncertain environments. I take inspiration from physics and neuroscience, and develop theory and (software) tools that allow for efficient, real-time interaction.

Hoang Minh Huu Nguyen
Hoang Minh Huu Nguyen
PhD student

I am a PhD candidate at the Signal Processing Systems group in TU Eindhoven working on Bayesian Machine Learning.

Wouter Nuijten
Wouter Nuijten
PhD Student

PhD student studying active inference as variational inference; core contributor to RxInfer.jl.

Mykola Lukashchuk
Mykola Lukashchuk
PhD student

I am a PhD candidate at the Electrical Engineering department, Eindhoven University of Technology.

Publications

Active Inference is a Subtype of Variational Inference
Automated decision-making under uncertainty requires balancing exploitation and exploration. Classical methods treat these separately …
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A Message Passing Realization of Expected Free Energy Minimization
We present a message passing approach to Expected Free Energy (EFE) minimization on factor graphs, based on the theory introduced in …
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A Factor Graph Approach to Variational Sparse Gaussian Processes
A Variational Sparse Gaussian Process (VSGP) is a sophisticated nonparametric probabilistic model that has gained significant …
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ExponentialFamilyManifolds.jl: Representing exponential families as Riemannian manifolds
ExponentialFamilyManifolds.jl implements exponential family natural parameter spaces as Riemannian manifolds, enabling geometric …
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GraphPPL.jl: A Probabilistic Programming Language for Graphical Models
This paper presents GraphPPL.jl, a novel probabilistic programming language designed for graphical models. GraphPPL.jl uniquely …
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Riemannian Black Box Variational Inference
We introduce Riemannian Black Box Variational Inference (RBBVI) for scenarios lacking gradient information of the model with respect to …
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Q-conjugate Message Passing for Efficient Bayesian Inference
Bayesian inference in nonconjugate models such as Bayesian Poisson regression often relies on computationally expensive Monte Carlo …
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Reactive Environments for Active Inference Agents with RxEnvironments.jl
Active Inference is a framework that emphasizes the interaction between agents and their environment. While the framework has seen …
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Gaussian Process Amplitude Demodulation by Message-Passing
Gaussian Process Amplitude Modulation (GPAM) is a probabilistic model that assigns Gaussian Process priors to the modulator and the …
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Efficient Model Evidence Computation in Tree-structured Factor Graphs
Model evidence is a fundamental performance measure in Bayesian machine learning as it represents how well a model fits an observed …
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Gaussian Process-based Amortization of Variational Message Passing Update Rules
Variational Message Passing facilitates automated variational inference in factorized probabilistic models where connected factors are …
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ReactiveMP.jl: A Julia package for reactive variational Bayesian inference
Variational Bayesian (VB) inference has become an increasingly popular method for approximating exact Bayesian inference in model-based …
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ReactiveMP.jl: A Julia package for reactive message passing-based Bayesian inference
ReactiveMP.jl is a native Julia implementation of reactive message passing-based Bayesian inference in probabilistic graphical models …
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