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Updated: Oct 19, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Non-convex Learning via Replica Exchange Stochastic Gradient MCMC.

Wei Deng1, Qi Feng2, Liyao Gao1

  • 1Purdue University, West Lafayette, IN, USA.

Proceedings of Machine Learning Research
|September 24, 2021
PubMed
Summary

Adaptive replica exchange SGMCMC (reSGMCMC) overcomes big data limitations of replica exchange Monte Carlo (reMC) for deep neural networks. This new method corrects biases, achieving state-of-the-art results in supervised and semi-supervised learning.

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Last Updated: Oct 19, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

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Area of Science:

  • Machine Learning
  • Computational Statistics
  • Deep Learning

Background:

  • Replica exchange Monte Carlo (reMC), or parallel tempering, accelerates Markov Chain Monte Carlo (MCMC) but struggles with large datasets.
  • Direct application of reMC to mini-batches introduces biases, hindering its use with stochastic gradient MCMC (SGMCMC) for deep neural networks (DNNs).

Purpose of the Study:

  • To develop a scalable and unbiased sampling method for deep neural networks using replica exchange principles.
  • To introduce an adaptive replica exchange SGMCMC (reSGMCMC) algorithm that automatically corrects for mini-batch biases.

Main Methods:

  • Proposed an adaptive replica exchange SGMCMC (reSGMCMC) algorithm.
  • Analyzed the properties and bias correction mechanisms of the reSGMCMC algorithm.
  • Conducted extensive empirical evaluations on benchmark datasets.

Main Results:

  • The proposed reSGMCMC algorithm effectively corrects biases introduced by mini-batch approximations.
  • Demonstrated an acceleration-accuracy trade-off in the numerical discretization of the underlying stochastic process.
  • Achieved state-of-the-art performance on CIFAR10, CIFAR100, and SVHN datasets for both supervised and semi-supervised learning tasks.

Conclusions:

  • reSGMCMC offers a scalable and accurate solution for MCMC sampling in deep learning.
  • The adaptive bias correction is crucial for extending reMC to stochastic settings.
  • This method advances the application of MCMC techniques to large-scale deep learning problems.