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10 years of news and resources for members of the IEEE Signal Processing Society
Latent variable models have two basic components: a latent structure encoding a hypothesized complex pattern and an observation model capturing the data distribution. With the advancements in machine learning and increasing availability of resources, the authors are able to perform inference in deeper and more sophisticated latent variable models. In most cases, these models are designed with a particular application in mind; hence, they tend to have restrictive observation models. The challenge, surfaced with the increasing diversity of data sets, is to generalize these latent models to work with different data types. The authors aim to address this problem by utilizing exponential dispersion models (EDMs) and proposing mechanisms for incorporating them into latent structures.
|Nominations Open for 2021 SPS Awards||1 September 2021|
|Call for Nominations: Awards Board and Nominations & Appointments Committee||24 September 2021|
|Meet the 2021 Candidates: IEEE President-Elect||1 October 2021|
|Call for Nominations for Editor-in-Chief, Open Journal of Signal Processing||1 October 2021|
|Election of President-Elect, Regional Directors-at-Large and Members-at-Large||1 October 2021|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2021|
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