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06/12/2023
Benchmarking Neural Network Training Algorithms
Training algorithms, broadly construed, are an essential part of every d...
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04/17/2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
The linearized-Laplace approximation (LLA) has been shown to be effectiv...
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08/02/2022
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
Neural operators are a type of deep architecture that learns to solve (i...
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05/20/2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Monte Carlo (MC) integration is the de facto method for approximating th...
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11/05/2021
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
Deep neural networks are prone to overconfident predictions on outliers....
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06/28/2021
Laplace Redux – Effortless Bayesian Deep Learning
Bayesian formulations of deep learning have been shown to have compellin...
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04/29/2020
Continual Deep Learning by Functional Regularisation of Memorable Past
Continually learning new skills is important for intelligent systems, ye...
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06/06/2019
