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Found 6 papers, 4 papers with code
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions
A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e. g. summing a periodic and linear kernel can capture seasonal variation with a long term trend.
Fast CNN-Based Object Tracking Using Localization Layers and Deep Features Interpolation
Moreover, bilinear interpolation is exploited to generate CNN feature maps of the training patches without actually forwarding the training patches through the network which achieves a significant reduction of the required computations.
Bayesian Neural Network Ensembles
Ensembles of neural networks (NNs) have long been used to estimate predictive uncertainty; a small number of NNs are trained from different initialisations and sometimes on differing versions of the dataset.
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Ensembling NNs provides an easily implementable, scalable method for uncertainty quantification, however, it has been criticised for not being Bayesian.
Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning
The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread.
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks.
