InfoQ

Google Researchers Propose Bayesian Teaching Method for Large Language Models

Google Research has proposed a training method that teaches large language models to approximate Bayesian reasoning by learning from the predictions of an optimal Bayesian system. The approach focuses ...
IEEE

Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Abstract: Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) ...
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Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination

This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), aself-supervised method for training motion planners to navigate environments withdense and dynamic obstacles. Classical ...
IEEE

Dynamic Bayesian Network Learning to Infer Sparse Models From Time Series Gene Expression Data

Abstract: One of the key challenges in systems biology is to derive gene regulatory networks (GRNs) from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic Bayesian networks ...
Phys.org

Self-learning neural network cracks iconic black holes

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...