Add to ORKGWe introduce a new, efficient, principled and backpropagation-compatible algorithm for learn-ing a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a com-pression cost, known as the variational free en-ergy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields comparable performance to dropout on MNIST classification. We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforce-ment learning. 1
Significant success has been reported recently ucsing deep neural networks for classification. Such ...
Designing uncertainty-aware deep learning models which are able to provide reasonable uncertainties ...
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navi...
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes....
Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-based methods, such as...
We derive global H 1 optimal training algorithms for neural networks. These algorithms guarantee t...
In this work, I will focus on ways in which we can build machine learning models that appropriately ...
Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-ar...
Buoyed by the success of deep multilayer neural networks, there is renewed interest in scalable lear...
Deep Learning-based models are becoming more and more relevant for an increasing number of applicati...
A novel method to propagate uncertainty through the soft-thresholding nonlinearity is proposed in th...
We derive global H^∞ optimal training algorithms for neural networks. These algorithms guarantee the...
Bayesian Neural Networks provide a tool to estimate the uncertainty of a neural network by consideri...
Abstracf- Proper initialization of neural networks is critical for a successful training of its weig...
We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks...
Significant success has been reported recently ucsing deep neural networks for classification. Such ...
Designing uncertainty-aware deep learning models which are able to provide reasonable uncertainties ...
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navi...
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes....
Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-based methods, such as...
We derive global H 1 optimal training algorithms for neural networks. These algorithms guarantee t...
In this work, I will focus on ways in which we can build machine learning models that appropriately ...
Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-ar...
Buoyed by the success of deep multilayer neural networks, there is renewed interest in scalable lear...
Deep Learning-based models are becoming more and more relevant for an increasing number of applicati...
A novel method to propagate uncertainty through the soft-thresholding nonlinearity is proposed in th...
We derive global H^∞ optimal training algorithms for neural networks. These algorithms guarantee the...
Bayesian Neural Networks provide a tool to estimate the uncertainty of a neural network by consideri...
Abstracf- Proper initialization of neural networks is critical for a successful training of its weig...
We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks...
Significant success has been reported recently ucsing deep neural networks for classification. Such ...
Designing uncertainty-aware deep learning models which are able to provide reasonable uncertainties ...
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navi...