论文:TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency 发表会议:ICLR2017 作者:Adji B. Dieng, Chong Wang, Jianfeng Gao, John Paisley 单位:1.Columbia University 2.Deep Learning Technology Center Microsoft Research 原文链接:TopicRNN: A Recurrent Neural Network with Long-Range Se...
其中,bb是偏置项,ϕ(⋅)ϕ(⋅)是激活函数,比如ReLU(许多研究者更喜欢使用hyperbolic tangent (tanh)作为RNN的激活函数。例如,可以参考Vu Pham等人的Dropout Improves Recurrent Neural Networks for Handwriting Recognition。不过,基于ReLU的RNN也是可以的,比如Quoc V. Le等人的论文A Simple Way to Initialize R...
摘要原文 We applied the generic neural network framework from Chap. 3 to specific network structures in the previous chapter. Multilayer Perceptrons and Convolutional Neural Networks fit squarely into that framework, and we were also able to modify it to capture Deep Auto-Encoders. We now extend...
循环神经网络(recurrent neural network)(RNN) 技术标签:机器学习深度学习 查看原文 阅读论文 《Train RNN as fast as CNN》 LSTM是如何优化的一样。SRU公式允许两个优化: - 第一个: 把公式1−31−3中的在批处理中的矩阵运算优化: UT= ⎜WWfWr⎞⎠⎟[x1,x2,…,xn] UT=(WWfWr)[x1,x...
a Recurrent Neural Network (RNN)-based statistical natural language generator that can learn to generate utterances directly from dialogue act – utterance pairs without any predefined syntaxes or semantic alignments. The presentation includes a systematic comparison of the principal RNN-based NLG models...
论文《Recurrent neural network based language model》简称RNNLM,作者Tomas Mikolov,经典的循环/递归神经语言模型。 2. 摘要 提出了一种新的基于递归神经网络的语言模型(RNN LM)及其在语音识别中的应用。 结果表明,与现有的退避语言模型相比,通过使用几个RNN LMs的混合,可以获得大约50%的困惑减少。
Recurrent neural network based language modelExtensions of Recurrent neural network based language modelGenerating Text with Recurrent Neural Networks 机器翻译(Machine Translation) 机器翻译是将一种源语言语句变成意思相同的另一种源语言语句,如将英语语句变成同样意思的中文语句。与语言模型关键的区别在于,...
论文原文:Generating Sequences With Recurrent Neural Networks 作者: Alex Graves Department of Computer Science University of Toronto graves@cs.toronto.edu Abstract This paper shows how Long Short-term Memory recurrent neural net- works can be used to generate complex sequences with long-range struc-...
论文《Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling》简称Attention BiRNN,作者Bing Liu(Electrical and Computer Engineering, Carnegie Mellon University)。经典的NLU论文(Semantic Frame)。 2. 摘要 基于注意力的编解码器神经网络模型最近在机器翻译和语音识别中显示出令...
2 The DRAW Network DRAW网络的基本结构类似于其他变分自动编码器的结构:编码器网络确定潜在代码的分布,以捕获有关输入数据的显着信息。解码器网络从代码分布中接收样本,并使用它们来调节自己在图像上的分布。但是,存在**三个主要差异**。首先,编码器和解码器都是DRAW中的循环网络,因此它们之间交换了一系列代码样本...