论文标题
潜在变量优化问题的截断推断：应用于强大的估计和学习
Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning
论文作者
论文摘要
除主要模型参数外，辅助潜在变量结构的优化问题经常出现在计算机视觉和机器学习中。附加的潜在变量使基础优化任务在内存（通过维护潜在变量）或运行时（重复对潜在变量的重复推断）而言，昂贵。我们旨在消除维持潜在变量的需求，并提出两种正式合理的方法，这些方法会动态调整潜在变量推理所需的准确性。这些方法具有大规模稳健估计和从标记数据中学习的基于能量的模型中的应用。
Optimization problems with an auxiliary latent variable structure in addition to the main model parameters occur frequently in computer vision and machine learning. The additional latent variables make the underlying optimization task expensive, either in terms of memory (by maintaining the latent variables), or in terms of runtime (repeated exact inference of latent variables). We aim to remove the need to maintain the latent variables and propose two formally justified methods, that dynamically adapt the required accuracy of latent variable inference. These methods have applications in large scale robust estimation and in learning energy-based models from labeled data.
