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Found 8 papers, 4 papers with code
Inference in conditioned dynamics through causality restoration
Computing observables from conditioned dynamics is typically computationally hard, because, although obtaining independent samples efficiently from the unconditioned dynamics is usually feasible, generally most of the samples must be discarded (in a form of importance sampling) because they do not satisfy the imposed conditions.
A Bayesian generative neural network framework for epidemic inference problems
The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies.
Relationship between fitness and heterogeneity in exponentially growing microbial populations
The constrained optimization of evolutionarily-motivated objective functions like the growth rate has emerged as the key theoretical assumption for the study of bacterial metabolism.
Epidemic mitigation by statistical inference from contact tracing data
We conclude that probabilistic risk estimation is capable to enhance performance of digital contact tracing and should be considered in the currently developed mobile applications.
Expectation propagation on the diluted Bayesian classifier
Efficient feature selection from high-dimensional datasets is a very important challenge in many data-driven fields of science and engineering.
Compressed sensing reconstruction using Expectation Propagation
Many interesting problems in fields ranging from telecommunications to computational biology can be formalized in terms of large underdetermined systems of linear equations with additional constraints or regularizers.
Loop corrections in spin models through density consistency
Computing marginal distributions of discrete or semidiscrete Markov random fields (MRFs) is a fundamental, generally intractable problem with a vast number of applications in virtually all fields of science.
A Max-Sum algorithm for training discrete neural networks
The algorithm we present performs as well as BP on binary perceptron learning problems, and may be better suited to address the problem on fully-connected two-layer networks, since inherent symmetries in two layer networks are naturally broken using the MS approach.
