İsmail Şenöz | BIASlab

A Factor Graph Approach to Variational Sparse Gaussian Processes

Wed, 02 Jul 2025 09:00:00 +0200

ExponentialFamilyManifolds.jl: Representing exponential families as Riemannian manifolds

Mon, 14 Apr 2025 16:37:31 +0200

Riemannian Black Box Variational Inference

Mon, 14 Oct 2024 09:00:00 +0200

Q-conjugate Message Passing for Efficient Bayesian Inference

Wed, 11 Sep 2024 09:00:00 +0200

Multi-Agent Trajectory Planning with NUV Priors

Wed, 10 Jul 2024 00:00:00 +0000

Gaussian Process Amplitude Demodulation by Message-Passing

Sun, 17 Sep 2023 00:00:00 +0000

Efficient Bayesian Inference by Conjugate-computation Variational Message Passing

Sat, 16 Sep 2023 18:07:00 +0200

Message Passing-based System Identification for NARMAX Models

Fri, 09 Dec 2022 09:00:00 +0200

Efficient Model Evidence Computation in Tree-structured Factor Graphs

Fri, 04 Nov 2022 00:00:00 +0000

Adaptive importance sampling message passing

Sun, 26 Jun 2022 00:00:00 +0000

Message Passing Algorithms for Hierarchical Dynamical Models

Fri, 24 Jun 2022 09:00:00 +0200

Probabilistic programming with stochastic variational message passing

Wed, 22 Jun 2022 09:00:00 +0200

Adaptive Optimizer Design for Constrained Variational Inference

Wed, 01 Jun 2022 00:00:00 +0000

Message Passing-Based Inference in the Gamma Mixture Model

Mon, 25 Oct 2021 00:00:00 +0000

Variational Log-Power Spectral Tracking for Acoustic Signals

Tue, 13 Jul 2021 00:00:00 +0000

The Switching Hierarchical Gaussian Filter

Mon, 12 Jul 2021 00:00:00 +0000

Variational Message Passing and Local Constraint Manipulation in Factor Graphs

Thu, 24 Jun 2021 16:37:31 +0200

Chance-constrained active inference

Thu, 06 May 2021 14:07:00 +0200

Online Message Passing-based Inference in the Hierarchical Gaussian Filter

Mon, 22 Jun 2020 00:00:00 +0000

Bayesian joint state and parameter tracking in autoregressive models

Mon, 08 Jun 2020 00:00:00 +0000

Online Variational Message Passing in the Hierarchical Gaussian Filter

Sun, 23 Sep 2018 18:07:00 +0200

ForneyLab: A Toolbox for Biologically Plausible Free Energy Minimization in Dynamic Neural Models

Sun, 23 Sep 2018 13:42:00 +0200

CoHear

Wed, 01 Feb 2017 00:00:00 +0000

Hearing loss is a very serious health condition that has been associated with early dementia and cognitive decline. Still, hearing aid market penetration is quite low, in particular for the large population that is afflicted with ‘mild-to-moderate’ hearing impairment. This is mainly due to two reasons: Stigma (association with old age) and hearing aids (HA) sound quality. The recent commercial introduction of fashionable ‘hearables’ will likely alleviate the stigma issue. Recent advances in machine learning and cloud computing open new avenues for attacking the sound quality issue for hearing aids. In this project, we intend to develop a (crowd-based) collaborative design approach to improving the sound quality issues for the mild-to-moderately hearing-impaired population.

We expect to build a working prototype for collaboratively designed hearing algorithms that can be applied to a new class of ‘smart hearing devices’ with high appeal to the mild-to-moderately hearing-impaired patient. As an additional benefit, we hope that our technology will ease the transition from hearables to professional hearing aid technology for the moderate-to-profound hearing-impaired population.