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[7] B. Liu and I. Lane, “Recurrent neural network structured output prediction for spoken language understanding,” in Proc. NIPS Workshop on Machine Learning for Spoken Language Understanding and Interactions, 2015. [8] D. Guo, G. Tur, W.-t. Yih, and G. Zweig, “Joint semantic utter...
1.1 为什么选择序列模型?(Why Sequence Models?) 1.2 数学符号(Notation) 1.3 循环神经网络模型(Recurrent Neural Network Model) 1.4 通过时间的反向传播(Backpropagation through time) 1.5 不同类型的循环神经网络(Different types of **RNN**...
Recently, many recurrent neural network based LM, a type of deep neural network for dealing with sequential data, have been proposed and achieved remarkable results. Most deep learning frame works, support the GPU to form fast models; in... SKY Donzia,Haeng-Kon Kim 被引量: 0发表: 2018年...
为了结合 recurrent structure and max-pooling layer,模型结合了recurrent neural models and convolutional neural models二者的优势,模型的时间复杂度是O(n),仅和文本长度线性相关。最后将模型和之前不同方法在中英文任务上进行对比,分类法包括主题分类,情感分类和写作类型分类。实验表明了本文模型在四个常见数据集上要...
梯度爆炸:处理梯度爆炸的问题相对梯度消失来说,就暴力了一些了,就是简单的对梯度的范式做个硬性限制《Statistical Language Models based on Neural Networks》,《 On the difficulty of training recurrent neural networks》 近些年,最成功的RNN变种结构来自于1997年的2篇论文: ...
第一周 循环序列模型(Recurrent Neural Networks) 为什么选择序列模型?(Why Sequence Models?) 在本课程中你将学会序列模型,它是深度学习中最令人激动的内容之一。循环神经网络(RNN)之类的模型在语音识别、自然语言处理和其他领域中引起变革。在本节课中,你将学会如何自行创建这些模型。我们先看一些例子,这些例子都有效...
Compared to non-sequence models, our model achieves 2% higher accuracy; with hierarchical attention, the proposed model can interpret the recommendation results effectively compared to naive RNN based models.Qiongjie YaoXiaofei LiaoHai JinIEEE Intl Conf on Ubiquitous Computing &ampampampampamp ...
Recurrent Neural Networks: Process Sequences one to one: Vanilla Neural Networks. raw one to many: eg. Image Captioning image -> sequence of words many to one: e.g. Sentiment Classification sequenc... 『 论文阅读』Attention-Based Recurrent Neural Network Models for Joint Intent Detection and ...
recurrent neural network based language model