Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights

D Soudry, I Hubara, R Meir - Advances in Neural Information …, 2014 - papers.nips.cc
Abstract Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-
based methods, such as BackPropagation (BP). Inference in probabilistic graphical models
is often done using variational Bayes methods, such as Expectation Propagation (EP). We ...

[PDF][PDF] Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights

D Soudry, I Hubara, R Meir - Advances in Neural …, 2014 - machinelearning.wustl.edu
Abstract Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-
based methods, such as BackPropagation (BP). Inference in probabilistic graphical models
is often done using variational Bayes methods, such as Expectation Propagation (EP). We ...

[PDF][PDF] Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights (with appendix)

D Soudry, I Hubara, R Meir - papers.nips.cc
Abstract Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-
based methods, such as BackPropagation (BP). Inference in probabilistic graphical models
is often done using variational Bayes methods, such as Expectation Propagation (EP). We ...

[PDF][PDF] Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights

D Soudry, I Hubara, R Meir - pdfs.semanticscholar.org
Abstract Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-
based methods, such as BackPropagation (BP). Inference in probabilistic graphical models
is often done using variational Bayes methods, such as Expectation Propagation (EP). We ...

[PDF][PDF] Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights (with appendix)

D Soudry, I Hubara, R Meir - researchgate.net
Abstract Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-
based methods, such as BackPropagation (BP). Inference in probabilistic graphical models
is often done using variational Bayes methods, such as Expectation Propagation (EP). We ...