Probabilistic Graphical Models
PulseAugur coverage of Probabilistic Graphical Models — every cluster mentioning Probabilistic Graphical Models across labs, papers, and developer communities, ranked by signal.
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New method tackles AI hallucinations in medical imaging with topological error regulation
Researchers have proposed a method to regulate "hallucinations" in medical AI by focusing on topological errors, which are more measurable than subjective inaccuracies. This approach involves rephrasing certain properti…
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New thermodynamic computing blueprint for energy-efficient ML
Researchers have proposed a new blueprint for thermodynamic computing that utilizes stochastic analog processes in physical hardware to address the growing energy and latency demands of machine learning. This approach f…
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Information Lattice Learning framed as PGM structure learning
A new paper introduces Information Lattice Learning (ILL) as a method for structure learning in probabilistic graphical models (PGMs). ILL learns interpretable rules by projecting signals onto a hierarchy of abstraction…
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New research explores synthetic data generation for fairness and privacy
Two research papers explore novel approaches to synthetic data generation (SDG) with a focus on fairness and privacy. The first paper revisits the concept of disparate impact in SDG, examining how approximation and esti…
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VEM algorithm scales to fit large nonlinear mixed effects models with over 15,000 parameters
Researchers have explored the Variational Expectation Maximization (VEM) algorithm as a scalable method for fitting Nonlinear Mixed Effects (NLME) models, particularly when dealing with a large number of parameters. Thi…
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GNNs enable Bayesian inversion for discrete structural component states
Researchers have developed a new Bayesian inversion framework using Probabilistic Graphical Models (PGMs) to infer the health states of structural components. This approach addresses challenges in formulating likelihood…