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Deep Neural Networks for Image-Based Dietary Assessment
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Learning Deep Generative Models With Doubly Stochastic Gradient MCMC.

Chao Du, Jun Zhu, Bo Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |July 6, 2017
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    Summary
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    We introduce doubly stochastic gradient MCMC, a new Bayesian inference method for deep generative models (DGMs). This approach enhances gradient estimation and handles complex models, outperforming existing techniques.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computational Statistics

    Background:

    • Deep generative models (DGMs) offer a principled way to understand data's causal factors.
    • Current research prioritizes variational inference, often making simplifying assumptions.
    • Bayesian deep generative models offer advantages, but Markov chain Monte Carlo (MCMC) methods are underexplored.

    Purpose of the Study:

    • To develop an efficient and scalable Bayesian inference method for deep generative models.
    • To extend Markov chain Monte Carlo (MCMC) methods to Bayesian DGMs.
    • To address limitations of current variational inference approaches in DGMs.

    Main Methods:

    • Introduced doubly stochastic gradient MCMC for approximate Bayesian inference in DGMs.
    • Employed mini-batch data sampling to estimate log-posterior gradients.
    • Utilized a neural adaptive importance sampler for intractable expectations over hidden variables.

    Main Results:

    • Demonstrated effectiveness of the proposed method on various DGMs.
    • Showcased performance across density estimation, data generation, and missing data imputation tasks.
    • Achieved superior results compared to state-of-the-art competitors.

    Conclusions:

    • Doubly stochastic gradient MCMC provides a powerful and generic framework for Bayesian inference in DGMs.
    • The method effectively learns DGMs and handles complex inference challenges.
    • Outperforms existing methods in key machine learning tasks.