Kernel mean embedding of distributions: A review and beyond
K Muandet, K Fukumizu… - … and Trends® in …, 2017 - nowpublishers.com
A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …
recently emerged as a powerful tool for machine learning and statistical inference. The basic …
[HTML][HTML] Efficient real-time monitoring of an emerging influenza pandemic: How feasible?
A prompt public health response to a new epidemic relies on the ability to monitor and
predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK …
predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK …
Comparing Methods for Estimating Marginal Likelihood in Symbolic Regression
Marginal likelihood has been proposed as a genetic programming-based symbolic
regression (GPSR) fitness metric to prevent overly complex expressions and overfitting …
regression (GPSR) fitness metric to prevent overly complex expressions and overfitting …
A semiautomatic method for history matching using sequential Monte Carlo
The aim of the history matching method is to locate nonimplausible regions of the parameter
space of complex deterministic or stochastic models by matching model outputs with data. It …
space of complex deterministic or stochastic models by matching model outputs with data. It …
Efficient real-time monitoring of an emerging influenza epidemic: how feasible?
A prompt public health response to a new epidemic relies on the ability to monitor and
predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK …
predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK …
New insights into history matching via sequential Monte Carlo
The aim of the history matching method is to locate non-implausible regions of the
parameter space of complex deterministic or stochastic models by matching model outputs …
parameter space of complex deterministic or stochastic models by matching model outputs …
Sampling sparse representations with randomized measurement langevin dynamics
Stochastic Gradient Langevin Dynamics (SGLD) have been widely used for Bayesian
sampling from certain probability distributions, incorporating derivatives of the log-posterior …
sampling from certain probability distributions, incorporating derivatives of the log-posterior …
[PDF][PDF] Biologically-plausible Markov Chain Monte Carlo Sampling from Vector Symbolic Algebra-encoded Distributions
Vector symbolic algebras (VSAs) are modelling frameworks that unify human cognition and
neural network models, and some have recently been shown to be probabilistic models akin …
neural network models, and some have recently been shown to be probabilistic models akin …
Machine learning for functional connectomics in Caenorhabditis elegans
A Warrington - 2021 - ora.ox.ac.uk
Santiago Ram ny Cajal first traced the microscopic intricacies of individual neurons in the
late 19th century. His work revolutionised neuroscience, and earned him the moniker “the …
late 19th century. His work revolutionised neuroscience, and earned him the moniker “the …