Search Results for author: Bert de Vries

Found 8 papers, 4 papers with code

Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization

1 code implementation17 Oct 2022 Jim Beckers, Bart van Erp, Ziyue Zhao, Kirill Kondrashov, Bert de Vries

Bayesian model reduction provides an efficient approach for comparing the performance of all nested sub-models of a model, without re-evaluating any of these sub-models.

AIDA: An Active Inference-based Design Agent for Audio Processing Algorithms

1 code implementation26 Dec 2021 Albert Podusenko, Bart van Erp, Magnus Koudahl, Bert de Vries

AIDA interprets searching for the "most interesting alternative" as an issue of optimal (acoustic) context-aware Bayesian trial design.

Reactive Message Passing for Scalable Bayesian Inference

1 code implementation25 Dec 2021 Dmitry Bagaev, Bert de Vries

We introduce Reactive Message Passing (RMP) as a framework for executing schedule-free, robust and scalable message passing-based inference in a factor graph representation of a probabilistic model.

Bayesian Inference

Active Inference and Epistemic Value in Graphical Models

no code implementations1 Sep 2021 Thijs van de Laar, Magnus Koudahl, Bart van Erp, Bert de Vries

The AIF literature describes multiple VFE objectives for policy planning that lead to epistemic (information-seeking) behavior.

Bayesian joint state and parameter tracking in autoregressive models

no code implementations L4DC 2020 Ismail Senoz, Albert Podusenko, Wouter M. Kouw, Bert de Vries

We address the problem of online Bayesian state and parameter tracking in autoregressive (AR) models with time-varying process noise variance.

A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms

1 code implementation8 Nov 2018 Marco Cox, Thijs van de Laar, Bert de Vries

This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms.

Probabilistic Programming Variational Inference

A Probabilistic Modeling Approach to Hearing Loss Compensation

no code implementations3 Feb 2016 Thijs van de Laar, Bert de Vries

Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the impairment of each specific patient.

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