Corpus ID: 8645175

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

@inproceedings{HernndezLobato2015ProbabilisticBF,
  title={Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks},
  author={Jos{\'e} Miguel Hern{\'a}ndez-Lobato and R. Adams},
  booktitle={ICML},
  year={2015}
}
  • José Miguel Hernández-Lobato, R. Adams
  • Published in ICML 2015
  • Computer Science, Mathematics
  • Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. [...] Key Method Similar to classical backpropagation, PBP works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients. A series of experiments on ten real-world datasets show that PBP is significantly faster than other techniques, while offering competitive predictive abilities. Our experiments also show…Expand Abstract
    Bayesian Recurrent Neural Networks
    • 89
    • PDF
    Assumed Density Filtering Methods for Learning Bayesian Neural Networks
    • 24
    • Highly Influenced
    • PDF
    Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
    • 80
    • PDF
    Natural-Parameter Networks: A Class of Probabilistic Neural Networks
    • 21
    • PDF
    Bayesian Dark Knowledge
    • 134
    • Highly Influenced
    • PDF
    Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
    • 898
    • Highly Influenced
    • PDF
    Bayesian dark knowledge
    • 79
    • Highly Influenced
    • PDF
    Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
    • 2,245
    • Highly Influenced
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 31 REFERENCES
    Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights
    • 162
    • Highly Influential
    • PDF
    Dropout: a simple way to prevent neural networks from overfitting
    • 19,156
    • PDF
    Bayesian learning for neural networks
    • 3,147
    • PDF
    A Practical Bayesian Framework for Backpropagation Networks
    • 2,070
    • PDF
    Practical Bayesian Optimization of Machine Learning Algorithms
    • 3,289
    • PDF
    Practical Variational Inference for Neural Networks
    • 651
    • PDF
    Learning representations by back-propagating errors
    • 16,037
    • PDF
    ImageNet classification with deep convolutional neural networks
    • 52,545
    • PDF
    Large-Scale Machine Learning with Stochastic Gradient Descent
    • 3,198
    • PDF