
Last Layer Marginal Likelihood for Invariance Learning
Data augmentation is often used to incorporate inductive biases into mod...
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Data augmentation in Bayesian neural networks and the cold posterior effect
Data augmentation is a highly effective approach for improving performan...
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BNNpriors: A library for Bayesian neural network inference with different prior distributions
Bayesian neural networks have shown great promise in many applications w...
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Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Deep Gaussian processes (DGPs) have struggled for relevance in applicati...
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GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a ...
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The Promises and Pitfalls of Deep Kernel Learning
Deep kernel learning and related techniques promise to combine the repre...
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Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
We propose a lower bound on the log marginal likelihood of Gaussian proc...
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Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian ...
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Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks
Infinite width limits of deep neural networks often have tractable forms...
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Design of Experiments for Verifying Biomolecular Networks
There is a growing trend in molecular and synthetic biology of using mec...
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Understanding Variational Inference in FunctionSpace
Recent work has attempted to directly approximate the `functionspace' o...
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A Bayesian Perspective on Training Speed and Model Selection
We take a Bayesian perspective to illustrate a connection between traini...
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Convergence of Sparse Variational Inference in Gaussian Processes Regression
Gaussian processes are distributions over functions that are versatile a...
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Variational Orthogonal Features
Sparse stochastic variational inference allows Gaussian process models t...
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Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
In image segmentation, there is often more than one plausible solution f...
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Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a pro...
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On the Benefits of Invariance in Neural Networks
Many real world data analysis problems exhibit invariant structure, and ...
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Capsule Networks – A Probabilistic Perspective
'Capsule' models try to explicitly represent the poses of objects, enfor...
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A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in largescale probl...
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Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
We implement gradientbased variational inference routines for Wishart a...
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Overcoming MeanField Approximations in Recurrent Gaussian Process Models
We identify a new variational inference scheme for dynamical systems who...
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Rates of Convergence for Sparse Variational Gaussian Process Regression
Excellent variational approximations to Gaussian process posteriors have...
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Translation Insensitivity for Deep Convolutional Gaussian Processes
Deep learning has been at the foundation of large improvements in image ...
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NonFactorised Variational Inference in Dynamical Systems
We focus on variational inference in dynamical systems where the discret...
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Bayesian Layers: A Module for Neural Network Uncertainty
We describe Bayesian Layers, a module designed for fast experimentation ...
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Closedform Inference and Prediction in Gaussian Process StateSpace Models
We examine an analytic variational inference scheme for the Gaussian Pro...
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Learning Invariances using the Marginal Likelihood
Generalising well in supervised learning tasks relies on correctly extra...
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Convolutional Gaussian Processes
We present a practical way of introducing convolutional structure into G...
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GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core c...
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Understanding Probabilistic Sparse Gaussian Process Approximations
Good sparse approximations are essential for practical inference in Gaus...
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Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models  a Gentle Tutorial
In this tutorial we explain the inference procedures developed for the s...
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Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Gaussian processes (GPs) are a powerful tool for probabilistic inference...
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Mark van der Wilk
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