【论文阅读】【CVPR2017】Temporal Attention-Gated Model for Robust Sequence Classification
Temporal Attention-Gated Model for Robust SequenceClassification 用于序列分类的典型技术是针对已经分割良好的序列而设计的，编辑删除嘈杂或不相关的部分。所以，这样的方法不能轻易应用于实际应用中预期的嘈杂序列。在本文中，我们介绍时间注意门控模型（TAGM）集成了注意模型和门控周期性神经网络的想法，以更好地处理噪声或非分段序列。具体而言，我
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相关文章
- 1. 论文阅读：《Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neu》
- 2. Spatio-Temporal Fusion based Convolutional Sequence Learning for Lip Reading论文阅读
- 3. 论文笔记：Connectionist Temporal Classification: Labelling Unsegmented Sequence
- 4. CTC算法论文阅读笔记：Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurren
- 5. Enriching Pre-trained Language Model with Entity Information for Relation Classification论文阅读
- 6. 【论文阅读笔记】Universal Language Model Fine-tuning for Text Classification
- 7. 论文阅读：《Convolutional Neural Networks for Sentence Classification》
- 8. Residual Attention Network for Image Classification 论文阅读
- 9. 论文笔记：SGM: Sequence Generation Model for Multi-label Classification
- 10. 论文解读：Sequence to Sequence Mixture Model for Diverse Machine Translation