This course will discuss the theory and application of algorithms for machine learning and inference, from an AI perspective. In this context, we consider as learning to draw conclusions from given ...

Variational inference (VI) plays an essential role in approximate Bayesian inference due to its computational efficiency and broad applicability. Crucial to the performance of VI is the selection of ...

SHENZHEN, China, May 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based on ...

Abstract: This article introduces a scalable distributed probabilistic inference algorithm for intelligent sensor networks, tackling challenges of continuous variables, intractable posteriors, and ...

ABSTRACT: In this paper, an alternating direction nonmonotone approximate Newton algorithm (ADNAN) based on nonmonotone line search is developed for solving inverse problems. It is shown that ADNAN ...

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference, previously implemented in MATLAB. VBMC is an approximate inference method ...

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...

The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm.