RecurrentNeuralNetworkRegularization 系统标签: regularizationneuralrecurrentrnnsdropoutlstms arXiv:1409.2329v1[cs.NE]8Sep2014RecurrentNeuralNetworkRegularizationWojciechZarembaWOJ.ZAREMBA@GMAILGoogle&NewYorkUniversityIlyaSutskeverILYASU@GOOGLEGoogleOriolVinyalsVINYALS@GOOGLEGoogleAbstractWepresentasimpleregularizationtechniq...
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we
查看原文 RNN 一、RNN概念 循环神经网络(RecurrentNeuralNetwork,RNN)是一类以序列(sequence)数据为输入,在序列的演进方向进行递归(recursion)且所有节点(循环单元)按链式连接的递归神经网络(recursiveneuralnetwork)。二、LSTM(Long Short Term Memory) 【Recurrent Neural Network Regularization】读后感(未编辑完毕) ...
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...
RECURRENT NEURAL NETWORK REGULARIZATION-笔记 RECURRENT NEURAL NETWORK REGULARIZATION 0 摘要 我们为带有长短期记忆(LSTM)unit的递归神经网络(RNN)提供了一种简单的正则化技术。 Dropout是用于规范化神经网络的最成功技术,不适用于RNN和LSTM。 在本文中,我们展示了如何正确地将缺失应用于LSTM,并表明它显着减少了在各种...
Voltage regularization Optimization with rewiring for sparse network connectivity 3 Supervised learning with e-prop Synaptic plasticity rules for e-prop in supervised learning Simulation details: framewise phoneme recognition task (Figure 2) Simulation details: phoneme sequence recognition with CTC (Figure 2...
由于循环神经网络(Recurrent Neural Network, RNN)自身的强自回归性,使得基于循环神经网络的变分自编码器更容易出现这种现象。针对这一问题,研究人员陆续提出多种解决方案[10-12]。在最近的研究中,Shen等人[13]利用多层卷积神经网络替代编码器并用循环网络作为解码器;Hao等人[14]使用循环模拟退火方法来缓解KL散度消失;...
RECURRENT NEURAL NETWORK REGULARIZATION-笔记 RECURRENT NEURAL NETWORK REGULARIZATION 0 摘要 我们为带有长短期记忆(LSTM)unit的递归神经网络(RNN)提供了一种简单的正则化技术。 Dropout是用于规范化神经网络的最成功技术,不适用于RNN和LSTM。 在本文中,我们展示了如何正确地将缺失应用于LSTM,并表明它显着减少了在各种...
RECURRENT NEURAL NETWORK REGULARIZATION-笔记 RECURRENT NEURAL NETWORK REGULARIZATION 0 摘要 我们为带有长短期记忆(LSTM)unit的递归神经网络(RNN)提供了一种简单的正则化技术。 Dropout是用于规范化神经网络的最成功技术,不适用于RNN和LSTM。 在本文中,我们展示了如何正确地将缺失应用于LSTM,并表明它显着减少了在各种...
RNN(recurrent neural network regularization) 论文:https://arxiv.org/pdf/1409.2329.pdf 摘要: 论文为RNN中的LSTM单元提出一个简单的调整技巧,dropout在调整神经网络中取得非常大的成功,但是在RNN(循环神经网络)和LSTM中表现不好。论文展示了如何正确的在LSTM中应用dropout,并且显示出该技巧能够显著减少过拟合现象。