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Found 10 papers, 4 papers with code
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
Here we introduce a novel deep learning architecture, the Fermionic Neural Network, as a powerful wavefunction Ansatz for many-electron systems.
Functional Regularisation for Continual Learning with Gaussian Processes
We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network.
Variational Bayesian dropout: pitfalls and fixes
Dropout, a stochastic regularisation technique for training of neural networks, has recently been reinterpreted as a specific type of approximate inference algorithm for Bayesian neural networks.
Gaussian Process Behaviour in Wide Deep Neural Networks
Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties.
Variational Gaussian Dropout is not Bayesian
Gaussian multiplicative noise is commonly used as a stochastic regularisation technique in training of deterministic neural networks.
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
However, they often do not capture their own uncertainties well making them less robust in the real world as they overconfidently extrapolate and do not notice domain shift.
GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end.
MCMC for Variationally Sparse Gaussian Processes
This paper simultaneously addresses these, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form.
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes
We then discuss augmented index sets and show that, contrary to previous works, marginal consistency of augmentation is not enough to guarantee consistency of variational inference with the original model.
Classification using log Gaussian Cox processes
McCullagh and Yang (2006) suggest a family of classification algorithms based on Cox processes.
