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Found 10 papers, 1 papers with code
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
We revisit the theoretical properties of Hamiltonian stochastic differential equations (SDES) for Bayesian posterior sampling, and we study the two types of errors that arise from numerical SDE simulation: the discretization error and the error due to noisy gradient estimates in the context of data subsampling.
Model Selection for Bayesian Autoencoders
We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization.
All You Need is a Good Functional Prior for Bayesian Deep Learning
This poses a challenge because modern neural networks are characterized by a large number of parameters, and the choice of these priors has an uncontrolled effect on the induced functional prior, which is the distribution of the functions obtained by sampling the parameters from their prior distribution.
Parametric Bootstrap Ensembles as Variational Inference
In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference.
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes
The Bayesian treatment of neural networks dictates that a prior distribution is considered over the weight and bias parameters of the network.
Sparse within Sparse Gaussian Processes using Neighbor Information
In this work, we address one limitation of sparse GPs, which is due to the challenge in dealing with a large number of inducing variables without imposing a special structure on the inducing inputs.
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
In this work we define a unified mathematical framework to deepen our understanding of the role of stochastic gradient (SG) noise on the behavior of Markov chain Monte Carlo sampling (SGMCMC) algorithms.
A Variational View on Bootstrap Ensembles as Bayesian Inference
In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification.
Property-driven State-Space Coarsening for Continuous Time Markov Chains
Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally.
