离散条件分布的光滑并元分割深度非参数估计
Deep Nonparametric Estimation of Discrete Conditional Distributions via Smoothed Dyadic Partitioning
摘要：
提出了一种离散条件概率分布的深度估计方法。这样的模型具有若干应用，包括音频、图像和视频数据的生成建模。我们的方法结合了两个主要的技术：二元分割和基于图形的平滑离散空间。通过递归地将每个维度分解为一系列二进制分裂，并使用基于图的趋势过滤对所得分布进行平滑，我们对模型施加了严格的结构，并实现了更高的样本效率。我们通过对合成数据集和真实数据集的一系列基准测试证明了我们模型的优势，在某些情况下，与文献中的其他流行方法相比，误差减少了近一半。我们所有的模型都在Tensorflow中实现，并在https://github.com/tansey/sdp上公开提供。
英文摘要：
We present an approach to deep estimation of discrete conditional probability distributions. Such models have several applications, including generative modeling of audio, image, and video data. Our approach combines two main techniques: dyadic partitioning and graph-based smoothing of the discrete space. By recursively decomposing each dimension into a series of binary splits and smoothing over the resulting distribution using graph-based trend filtering, we impose a strict structure to the model and achieve much higher sample efficiency. We demonstrate the advantages of our model through a series of benchmarks on both synthetic and real-world datasets, in some cases reducing the error by nearly half in comparison to other popular methods in the literature. All of our models are implemented in Tensorflow and publicly available at https://github.com/tansey/sdp .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
