概率序列模型的压缩推理

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摘要翻译：
隐马尔可夫模型(HMMs)和条件随机场(CRFs)是两种常用的序列数据建模技术。在CRFs和HMMs上设计的推理算法允许在给定观测数据的情况下估计状态序列。在一些应用中，状态序列的估计不是最终目标；相反，目标是计算它的一些函数。在这种情况下，通过传统的推理技术估计状态序列，然后根据估计计算功能映射并不一定是最优的。一种更正式的方法是直接从观察结果推断最终结果。特别地，我们考虑了问题的具体实例，其中目标是寻找没有精确过渡点的状态轨迹，并导出了一种新的多项式时间推理算法，该算法优于vanilla推理技术。我们指出了这个特殊问题在许多不同的应用中普遍存在，并给出了其中三个方面的实验：（1）玩具机器人跟踪；（2）单笔画字符识别；（3）手写体字识别。
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英文标题：
《Compressed Inference for Probabilistic Sequential Models》
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作者：
Gungor Polatkan, Oncel Tuzel
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Hidden Markov models (HMMs) and conditional random fields (CRFs) are two popular techniques for modeling sequential data. Inference algorithms designed over CRFs and HMMs allow estimation of the state sequence given the observations. In several applications, estimation of the state sequence is not the end goal; instead the goal is to compute some function of it. In such scenarios, estimating the state sequence by conventional inference techniques, followed by computing the functional mapping from the estimate is not necessarily optimal. A more formal approach is to directly infer the final outcome from the observations. In particular, we consider the specific instantiation of the problem where the goal is to find the state trajectories without exact transition points and derive a novel polynomial time inference algorithm that outperforms vanilla inference techniques. We show that this particular problem arises commonly in many disparate applications and present experiments on three of them: (1) Toy robot tracking; (2) Single stroke character recognition; (3) Handwritten word recognition.
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PDF链接：
https://arxiv.org/pdf/1202.3759

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关键词：observations Applications Conventional Intelligence Presentation 目标 玩具 HMMs 识别 observations