The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics ...

Diffusion probabilistic models (DPMs) have achieved impressive success in high-resolution image synthesis, especially in recent large-scale text-to-image generation applications. An essential ...

Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how ...

ELEC_ENG 302: Probabilistic Systems VIEW ALL COURSE TIMES AND SESSIONS Prerequisites Math 228-2 or equivalent Description Introduction to probability theory and its applications. Axioms of probability ...

Massively parallel probabilistic computing with sparse Ising machines. Nature Electronics, 2022; DOI: 10.1038/s41928-022-00774-2 ...

Probabilistic safety assessments (PSAs) are recognized as an important tool for assessing the level of safety for nuclear power plants (NPPs). In particular, the Level 2 PSA for NPPs provides key ...

Probabilistic computing, using the Boltzmann law, claims to be 100 million times more energy-efficient than current NVIDIA GPUs by using noise for complex calculations.

A’s, Bayes, C’s By studying babies and young children, scientists can test whether probabilistic reasoning is present before life experiences begin sculpting the mind.

Probabilistic Chips Krishna Palem thinks a little uncertainty in chips could extend battery life in mobile devices–and maybe the duration of Moore’s Law, too. by Erika Jonietz February 19, 2008 ...