论文标题
准合并Langevin变异自动编码器
Quasi-symplectic Langevin Variational Autoencoder
论文作者
论文摘要
变性自动编码器（VAE）是神经学习研究中非常流行且经过深入研究的生成模型。为了在处理庞大维度的大量数据集的实际任务中利用VAE，需要处理构建低差异证据较低界限（ELBO）的困难。 Markov Chain Monte Carlo（MCMC）是拧紧ELBO的有效方法，用于近似后验分布，而Hamiltonian变异自动编码器（HVAE）是一种有效的MCMC启发方法，用于构建可重新处理技巧的低变义ELBO。 HVAE将汉密尔顿动态流动转化为变异推断，从而显着改善了后验估计的性能。我们在这项工作中提出了一种基于Langevin动态流动的推理方法，通过通过Langevin Dynamic将梯度信息纳入推理过程中，这是一种基于MCMC的方法，类似于HVAE。具体而言，我们采用准合并积分器来应对天真兰格文流中黑森西亚计算的禁止问题。我们通过其他基于梯度流的方法来显示所提出的框架的理论和实际有效性。
Variational autoencoder (VAE) is a very popular and well-investigated generative model in neural learning research. To leverage VAE in practical tasks dealing with a massive dataset of large dimensions, it is required to deal with the difficulty of building low variance evidence lower bounds (ELBO). Markov Chain Monte Carlo (MCMC) is an effective approach to tighten the ELBO for approximating the posterior distribution and Hamiltonian Variational Autoencoder (HVAE) is an effective MCMC inspired approach for constructing a low-variance ELBO that is amenable to the reparameterization trick. The HVAE adapted the Hamiltonian dynamic flow into variational inference that significantly improves the performance of the posterior estimation. We propose in this work a Langevin dynamic flow-based inference approach by incorporating the gradients information in the inference process through the Langevin dynamic which is a kind of MCMC based method similar to HVAE. Specifically, we employ a quasi-symplectic integrator to cope with the prohibit problem of the Hessian computing in naive Langevin flow. We show the theoretical and practical effectiveness of the proposed framework with other gradient flow-based methods.
