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Found 5 papers, 4 papers with code
TCE: A Test-Based Approach to Measuring Calibration Error
This paper proposes a new metric to measure the calibration error of probabilistic binary classifiers, called test-based calibration error (TCE).
Generalised Bayesian Inference for Discrete Intractable Likelihood
Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly infinite sets, which can be impractical.
Robust Generalised Bayesian Inference for Intractable Likelihoods
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specification of the likelihood.
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
Our approach constructs a prior distribution for the parameters of the network, called a ridgelet prior, that approximates the posited Gaussian process in the output space of the network.
The global optimum of shallow neural network is attained by ridgelet transform
We prove that the global minimum of the backpropagation (BP) training problem of neural networks with an arbitrary nonlinear activation is given by the ridgelet transform.
