自动编码变分贝叶斯推理主题和可视化
Auto-Encoding Variational Bayes for Inferring Topics and Visualization
摘要：
可视化和主题建模是文本分析中广泛使用的方法。传统的可视化方法在可视化空间（通常为2D或3D）中找到文档的低维表示，可以使用散点图显示。相比之下，主题建模旨在从文本中发现主题，但对于可视化，需要使用降维方法执行事后嵌入。最近的方法提出使用生成模型来共同寻找主题和可视化，允许语义注入到可视化空间中进行有意义的解释。阻止这些方法实际使用的一个主要挑战是它们的推理算法的可扩展性。我们目前，据我们所知，第一个快速的自动编码变分贝叶斯推理方法，共同推断主题和可视化。由于我们的方法是黑盒，它可以有效地处理模型的变化，很少的数学重新推导的努力。我们在真实世界的大型数据集上证明了我们的方法的效率和有效性，并将其与现有的基线进行了比较。
英文摘要：
Visualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from text, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative model to jointly find topics and visualization, allowing the semantics to be infused in the visualization space for a meaningful interpretation. A major challenge that prevents these methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black box, it can handle model changes efficiently with little mathematical rederivation effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines.
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。
