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Found 12 papers, 6 papers with code
A Probabilistic Modeling Approach to Hearing Loss Compensation
Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the impairment of each specific patient.
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
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.
Bayesian joint state and parameter tracking in autoregressive models
We address the problem of online Bayesian state and parameter tracking in autoregressive (AR) models with time-varying process noise variance.
On Preference Learning Based on Sequential Bayesian Optimization with Pairwise Comparison
To efficiently learn the preferences and reduce search space quickly, we propose the agent that interacts with the user to collect the most informative data for learning.
Active Inference and Epistemic Value in Graphical Models
The AIF literature describes multiple VFE objectives for policy planning that lead to epistemic (information-seeking) behavior.
Reactive Message Passing for Scalable Bayesian Inference
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.
AIDA: An Active Inference-based Design Agent for Audio Processing Algorithms
AIDA interprets searching for the "most interesting alternative" as an issue of optimal (acoustic) context-aware Bayesian trial design.
Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization
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.
Realising Synthetic Active Inference Agents, Part II: Variational Message Updates
The Free Energy Principle (FEP) describes (biological) agents as minimising a variational Free Energy (FE) with respect to a generative model of their environment.
Automating Model Comparison in Factor Graphs
Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages.
Realising Synthetic Active Inference Agents, Part I: Epistemic Objectives and Graphical Specification Language
Active Inference (AIF) is a corollary of the FEP that specifically details how systems that are able to plan for the future (agents) function by minimising particular free energy functionals that incorporate information seeking components.
Toward Design of Synthetic Active Inference Agents by Mere Mortals
The theoretical properties of active inference agents are impressive, but how do we realize effective agents in working hardware and software on edge devices?
