联结主义时间分类（Connectionist temporal classification）的论文笔记

前言：

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最近在github上更新了一些代码，但没在这里更新文章，这次就在这写一篇论文的阅读笔记。

论文是《Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural 'networks》（Alex Graves, Santiago Fernández,Faustino J. Gomez, Jürgen Schmidhuber，https://www.researchgate.net/publication/221346365_Connectionist_temporal_classification_Labelling_unsegmented_sequence_data_with_recurrent_neural_%27networks），主题是序列识别的方法。

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正文：

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首先，论文的第一节Introduction提到了之前方法的问题：

1. they usually require a significant amount of task specific knowledge, e.g. to design the state models for HMMs, or choose the input features for CRFs; (2)they require explicit (and often questionable) dependency assumptions to make inference tractable, e.g. the assumption that observations are independent for HMMs; (3) for standard HMMs, training is generative, even though sequence labelling is discriminative.

    （1）通常这些方法需要很多任务专属的知识，比如说需要为HMMs设计状态模型，或为CRFs选择输入特征；（2）需要为