Probabilistic Inference in the Era of Tensor Networks and Differential Programming

Autor: Roa-Villescas, Martin, Gao, Xuanzhao, Stuijk, Sander, Corporaal, Henk, Liu, Jin-Guo

Publikováno v: Phys. Rev. Res. 6, 033261 (2024)

Probabilistic inference is a fundamental task in modern machine learning. Recent advances in tensor network (TN) contraction algorithms have enabled the development of better exact inference methods. However, many common inference tasks in probabilis

Externí odkaz: http://arxiv.org/abs/2405.14060

Real-time audio processing for hearing aids using a model-based Bayesian inference framework

Autor: Roa Villescas, Martin, de Vries, A. (Bert), Stuijk, Sander, Corporaal, Henk

Publikováno v: Proceedings of the 23rd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2020: SCOPES 2020, 82-85
STARTPAGE=82;ENDPAGE=85;TITLE=Proceedings of the 23rd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2020
SCOPES

Development of hearing aid (HA) signal processing algorithms entails an iterative process between two design steps, namely algorithm development and the embedded implementation. Algorithm designers favor high-level programming languages for several r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96ba35d41fcc117e1f5bbe5fceffbe75
https://research.tue.nl/nl/publications/7c1f548e-dccd-40d0-8a7f-399e2abbd6fd