© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the va...
We reinterpret multiplicative noise in neural networks as auxiliary random variables that augment th...
Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights in...
We consider the problem of Bayesian parameter estimation for deep neural networks, which is importan...
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal wit...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors a...
Variational inference (VI) or Variational Bayes (VB) is a popular alternative to MCMC, which doesn\u...
Deep Learning-based models are becoming more and more relevant for an increasing number of applicati...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
In this work, I will focus on ways in which we can build machine learning models that appropriately ...
Deep neural networks have bested notable benchmarks across computer vision, reinforcement learning, ...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
We propose a new variational family for Bayesian neural networks. We decompose the variational poste...
Dropout, a stochastic regularisation technique for training of neural networks, has recently been re...
Dropout, a stochastic regularisation technique for training of neural networks, has recently been re...
We reinterpret multiplicative noise in neural networks as auxiliary random variables that augment th...
Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights in...
We consider the problem of Bayesian parameter estimation for deep neural networks, which is importan...
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal wit...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors a...
Variational inference (VI) or Variational Bayes (VB) is a popular alternative to MCMC, which doesn\u...
Deep Learning-based models are becoming more and more relevant for an increasing number of applicati...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
In this work, I will focus on ways in which we can build machine learning models that appropriately ...
Deep neural networks have bested notable benchmarks across computer vision, reinforcement learning, ...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
We propose a new variational family for Bayesian neural networks. We decompose the variational poste...
Dropout, a stochastic regularisation technique for training of neural networks, has recently been re...
Dropout, a stochastic regularisation technique for training of neural networks, has recently been re...
We reinterpret multiplicative noise in neural networks as auxiliary random variables that augment th...
Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights in...
We consider the problem of Bayesian parameter estimation for deep neural networks, which is importan...