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 R
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任务上的过度拟合。这些任务包括语言建模,语音...
Then, we formalize the problem and describe our regularization term by which the learning objective of the Factored Tensor Recurrent Neural Network is extended. Finally, we demonstrate its effectiveness on the cart-pole and mountain car benchmarks....
Recurrent Neural Network Regularization We let subscripts denote timesteps and superscripts denote layers. All our states are n-dimensional. Let h l t ∈ R n be a hidden state in layer l in timestep t. Moreover, let T n,m : R n →R m be a linear transformwith a bias (Wx+b for...
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
Recurrent neural network regularization. arXiv preprint arXiv:1409.2329, 2014.[2] Yarin Gal and Zoubin Ghahramani. A theoretically grounded application of dropout in recurrent neural networks. In Advances in Neural Information Processing Systems, pp. 1019–1027, 2016.[3] Tomas Mikolov, Martin Karafi...
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;...
4“Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling,” H. Sak et al. (2014). 5“Recurrent Neural Network Regularization,” W. Zaremba et al. (2015). 6“Recurrent Nets that Time and Count,” F. Gers and J. Schmidhuber (2000). 7“Learning ...
Dropout prevents co-adaptation of these hidden units by sampling from an exponential number of different “thinned” networks, thus reduces overfitting and leads to significant improvements over other regularization methods [52]. 4.4. Segment level LSTM for relation classification The formulation of ...
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...