这篇论文提出了一种新的贝叶斯非参数学习方法PYPM-GGD，用自适应步长处理非共轭后验，在MIT67和SUN397上聚类效果不错，比传统SVI更灵活。
传统随机变分推理（SVI）依赖共轭后验假设，难以处理非共轭情形。新方法PYPM-GGD基于常数步长随机梯度上升，无需闭式变分后验期望，仅要求可微分。借鉴Adam引入自适应步长，提升随机设置下的收敛性。在MIT67和SUN397数据集上使用ResNet特征，与深度聚类算法相比，聚类指标达到或超越现有最佳方法。
PYPM-GGD: Pitman-Yor Process Mixture with Generalized Gaussian Density using ADAM
Large scale Bayesian nonparametrics (BNP) learner such as Stochastic Variational Inference (SVI) can handle datasets with large class number and large training size at fractional cost. Like its predecessor, SVI rely on the assumption of conjugate variational posterior to approximate the true posterior. A more challenging problem is to consider large scale learning on non-conjugate posterior. Recent works in this direction are mostly associated with using Monte Carlo methods for approximating the learner. However, these works are usually demonstrated on non-BNP related task and less complex models such as logistic regression, due to higher computational complexity. In order to overcome the issue faced by SVI, we develop a novel approach based on the recently proposed constant stepsize stochastic gradient ascent to allow large scale learning on non-conjugate posterior. Unlike SVI, our new learner does not require closed- form expression for the variational posterior expectatations. Our only requirement is that the variational posterior is differentiable. In order to ensure convergence in stochastic settings, SVI rely on decaying step-sizes to slow its learning. Inspired by SVI and Adam, we propose the novel use of adaptive stepsizes in our method to significantly improve its learning. We show that our proposed methods is compatible with ResNet features when applied to large class number datasets such as MIT67 and SUN397. Finally, we compare our proposed learner with several recent works such as deep clustering algorithms and showed we were able to produce on-par or outperform the state-of-the-art methods in terms of clustering measures.