Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool to estimate the uncertainty of a neural network by sampling different models for each input. Variational Neural Networks (VNNs) consider a probability distribution over each layer's outputs and generate parameters for it with the corresponding sub-layers. We provide two Python implementations of VNNs with PyTorch and JAX machine learning libraries that ensure reproducibility of the experimental results and allow implementing uncertainty estimation methods easily in other projects.publishedVersionPeer reviewe
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learn-ing a p...
Estimating the uncertainty in deep neural network predictions is crucial for many real-world applica...
Bayesian neural network models for probabilistic VTEC forecasting with 95% confidence, from the pape...
Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool t...
Bayesian Neural Networks provide a tool to estimate the uncertainty of a neural network by consideri...
In this work, I will focus on ways in which we can build machine learning models that appropriately ...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal wit...
The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to bu...
Neural networks are flexible models capable of capturing complicated data relationships. However, ne...
Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the pr...
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to ...
Bayesian neural networks are powerful inference methods by accounting for randomness in the data and...
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes....
We release a new Bayesian neural network library for PyTorch for large-scale deep networks. Our libr...
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learn-ing a p...
Estimating the uncertainty in deep neural network predictions is crucial for many real-world applica...
Bayesian neural network models for probabilistic VTEC forecasting with 95% confidence, from the pape...
Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool t...
Bayesian Neural Networks provide a tool to estimate the uncertainty of a neural network by consideri...
In this work, I will focus on ways in which we can build machine learning models that appropriately ...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal wit...
The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to bu...
Neural networks are flexible models capable of capturing complicated data relationships. However, ne...
Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the pr...
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to ...
Bayesian neural networks are powerful inference methods by accounting for randomness in the data and...
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes....
We release a new Bayesian neural network library for PyTorch for large-scale deep networks. Our libr...
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learn-ing a p...
Estimating the uncertainty in deep neural network predictions is crucial for many real-world applica...
Bayesian neural network models for probabilistic VTEC forecasting with 95% confidence, from the pape...