Apr 13, 2017 · We show that constant SGD can be used as an approximate Bayesian posterior inference algorithm. Specifically, we show how to adjust the tuning parameters.

Stochastic gradient descent (SGD) has become crucial to modern machine learning. SGD optimizes a function by following noisy gradients with a decreasing step ...

Stochastic Gradient Descent with a constant learning rate (constant SGD) simulates a Markov chain with a stationary distribution.

Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks · Computer Science, Mathematics. 2018 Information Theory ...

Apr 17, 2017 · We show that constant SGD can be used as an approximate Bayesian posterior inference algorithm. Specifically, we show how to adjust the tuning parameters of ...

Sep 10, 2024 · Stochastic Gradient Descent with a constant learning rate (constant SGD) simulates a Markov chain with a stationary distribution.

(1) The document analyzes stochastic gradient descent (SGD) as an approximate Bayesian inference algorithm. (2) It shows that constant SGD simulates a Markov ...

Stochastic gradient descent as approximate bayesian inference. Journal of Machine Learning Research, 18(134):1–35,. 2017. Claire P Massen and Jonathan PK ...

Duration: 24:34
Posted: Apr 5, 2018

We analyzed stochastic gradient descent as an approximate. Bayesian inference algorithm, deriving optimal constant learning rates and preconditioning ...