13 jan 2021 · riving stochastic backpropagation rules for any distribution, In our case, we substitute the normal prior and posterior on the weights
Generalized_Stochastic_Backpropagation.pdf
the direction of the gradient of the loss made by the classic backpropagation algorithm PBP uses the following prop- erty of Gaussian distributions (Minka,
lobato2015probabilistic.pdf
Equation (9) can be interpreted as a modified backpropagation rule for Gaussian distributions that takes into account the gradients through the mean µ and
rezende14.pdf
weights given the data using a “mean-field” factorized distribution, in an online setting if we average the MNN output using the inferred posterior
5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf
We propose that the back propagation algorithm for super- view, fitting output to input using normal distributions and varying
3-supervised-learning-of-probability-distributions-by-neural-networks.pdf
10 avr 1992 · in contrast to previous approaches which approximate the posterior weight distribution by a Gaussian In this work, the Hybrid Monte Carlo
bbp.pdf
with incrementally backpropagation-based training to improve generalization A Gaussian function expresses the normal distribution, an
MEIC-84995-Ricardo-Ponciano-Resumo.pdf