Uncertainty is an intrinsic part of neural computation, whether for sensory processing, motor control or cognitive reasoning. For instance, it is impossible to determine with certainty the age of a ...

Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

Probabilistic Graphical Models (PGMs) are a popular way of portraying condi- tional dependencies between random variables (randvars) of a complex proba- bility distribution. One of the main purposes ...

How do human beings perceive their environment and make their decisions? To successfully interact with the immediate environment, for human beings it is not enough to have basic evidence of the world ...

Even though PGMs reduce memory complexity of full joint distributions and can therefore make inference algorithms like variable elimination or the junction tree algorithm tractable in some cases, the ...

Revolutionary Analog-based Probabilistic Inference Devices for Unconventional Processing of Signals for Intelligent Data Exploitation. OK, so it’s no RADAR, SONAR or LASER (invented by the same folks ...

Alex Burmester does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond ...

The Department of Computer Science, Faculty of Science, University of Helsinki invites applications for a Doctoral or Postdoctoral Researcher in Resource-Efficient Probabilistic Machine Learning. The ...