Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropou
(研究会輪読) Weight Uncertainty in Neural Networks
Bayes by Backprop is a method for introducing weight uncertainty into neural networks using variational Bayesian learning. It represents each weight as a probability distribution rather than a fixed value. This allows the model to better assess uncertainty. The paper proposes Bayes by Backprop, which uses a simple approximate learning algorithm similar to backpropagation to learn the distributions
（DL hacks輪読）Bayesian Neural Network
The document discusses Bayesian neural networks and related topics. It covers Bayesian neural networks, stochastic neural networks, variational autoencoders, and modeling prediction uncertainty in neural networks. Key points include using Bayesian techniques like MCMC and variational inference to place distributions over the weights of neural networks, modeling both model parameters and prediction
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Implementing Dropout as a Bayesian Approximation in TensorFlow
