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

Latent space models are popular for analyzing dynamic network data. We propose a variational approach to estimate the model parameters and the latent positions of the nodes in the network. The ...

We provide statistical guarantees for a family of variational approximations to Bayesian posterior distributions, called α-VB, which has close connections with variational approximations of tempered ...

The Department of Computer Science, Faculty of Science, University of Helsinki invites applications for a Postdoctoral Researcher in Probabilistic Machine Learning and Amortized Inference. The ...

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 ...