AReviewofRecurrentNeuralNetworks: LSTMCellsandNetworkArchitectures YongYu yuyongep@163 DepartmentoAutomation,Xi’anInstituteoHigh-Technology,Xi’an710025, China,andInstituteNo.25,SecondAcademyoChina,AerospaceScience andIndustryCorporation,Beijing100854,China XiaoshengSi sxs09@mails.tsinghua.edu ChanghuaHu hch_re...
使用ReLU可以使得权重不爆炸也不消失。 2)使用Truncated BPTT,能够缓解这一问题(LSTM就是用了BP和TBPTT进行训练的)。 3)LSTM是通过设计好的含有回复式的结点以及固定的权重来解决这个问题的。LSTM论文中叫CEC(恒定误差传送带) 注:LSTM同时用了设定恒定的权重以及TBPTT来缓解梯度爆炸或者消失这个问题并不冲突哈 问题...
摘要: Recurrent neural networks RNNs have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are u...DOI: 10.1162/neco_a_01199 年份: 2019 ...
Graph Neural Networks: A Review of Methods and Applications阅读笔记 词汇解释 拉普拉斯矩阵:图的一种表示方式。详细可参考: https://blog.csdn.net/qq_30159015/article/details/83271065 https://zhuanlan.zhihu.com/p/84649941 graph reasoning models(图推理模型) graph Embedding(图嵌入)-它学习用低维向量表示...
《A review of irregular time series data handling with gated recurrentneural networks》 这篇的主要贡献,一个是对时序数据插补的技术做了一个比较好的总结,一个是对天然不规则数据上的处理方法做了比较好的总结,最后就是大量魔改的循环神经网络模型的总结。虽然很多都没看过也不懂,但是我大受震撼。
2015. A critical review of recur- rent neural networks for sequence learning. CoRR, abs/1506.00019.Lipton Z C, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning{J}. arXiv preprint arXiv:1506.00019, 2015....
回声状态网络(Echo State Networks,ESNs)ESNs是一种递归神经网络(Recurrent Neural Network,RNN),ESNs被视为梯度下降训练的RNN的替代方法。全面介绍了ESN的设计和应用,并将其分为经典ESN、DeepESN和组合模型。 在深度学习的背景下,BP(误差反向传播)是RNN训练中最重要的成就之一,但只取得了部分成功。其局限性之一是...
While traditionaldeep learningnetworks assume that inputs and outputs are independent of each other, the output of recurrent neural networks depend on the prior elements within the sequence. While future events would also be helpful in determining the output of a given sequence, unidirectional recurren...
A review of recurrent neural networks: LSTM cells and network architectures Neural Comput. Neural Comput., 31 (7) (2019), pp. 1235-1270, 10.1162/neco_a_01199 View in ScopusGoogle Scholar [13] Laguna P., Mark R.G., Goldberger A.L., Moody G.B. A database for evaluation of algorithms...
回顾神经元网络不仅在输入空间上进行操作,而且在内部状态空间进行操作(一条已经被网络处理过的轨迹(a trace of what already been processed by the network,往下看就理解什么意思)),从这个意义上,回归神经元网络与前馈网络结构有本质的不同。这等价于一个迭代函数系统(IFS; see (Barnsley, 1993) for a general ...