))
Bayesian Active Learning in Audiology
Manage episode 325925475 series 3339931
内容由Jan-Willem Wasmann提供。所有播客内容（包括剧集、图形和播客描述）均由 Jan-Willem Wasmann 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品，您可以按照此处概述的流程进行操作https://zh.player.fm/legal。
Here we discuss with Josef Schlittenlacher (ManCAD), Bert de Vries (TUe) and Dennis Barbour (WashU st. Louis) the potential of Bayesian active learning in audiology, in medicine, and beyond.
Quotes from the interview:
Dennis: 'No Bayesianists are born, they are all converted' (origin unknown)
Josef: The audiogram is the ideal testbed for Bayesian active learning.'
Bert's favorite quote: “Everything is the way it is because it got that way” (D'Arcy Wentworth Thompson, 1860--1948)
The later quote reflects on the idea that everything evolved to where it is now. It’s not a quote from the Free Energy Principle but it has everything to do with it. The hearing system evolved to where it is now. To design proper hearing aid algorithms, we should not focus on the best algorithm but rather on an adaptation process that converges to better algorithms than before.
Further reading and exploring:
- https://computationalaudiology.com/bayesian-active-learning-in-audiology/
- https://computationalaudiology.com/for-professionals/
- Audiogram estimation using Bayesian active learning, https://doi.org/10.1121/1.5047436
- Online Machine Learning Audiometry, https://pubmed.ncbi.nlm.nih.gov/30358656/
- Bayesian Pure-Tone Audiometry Through Active Learning Under Informed Priors, https://www.frontiersin.org/articles/10.3389/fdgth.2021.723348/full
- Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review, https://www.jmir.org/2022/2/e32581
3集单集
分享
