순환 인공 신경망

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Pythagoras0 (토론 | 기여)님의 2020년 12월 26일 (토) 05:10 판 (→‎메타데이터: 새 문단)
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  • Recurrent neural networks (RNN) are a class of neural networks that are helpful in modeling sequence data.[1]
  • Derived from feedforward networks, RNNs exhibit similar behavior to how human brains function.[1]
  • In a RNN the information cycles through a loop.[1]
  • A recurrent neural network, however, is able to remember those characters because of its internal memory.[1]
  • Recurrent neural networks (RNNs) are a form of a neural network that recognizes patterns in sequential information via contextual memory.[2]
  • RNNs can be contrasted with simple feed forward neural networks.[2]
  • However at present RNNs are more widely used in areas of radiology related to language.[2]
  • In this work, we propose a novel recurrent neural network (RNN) architecture.[3]
  • {In this work, we propose a novel recurrent neural network (RNN) architecture.[3]
  • - In this work, we propose a novel recurrent neural network (RNN) architecture.[3]
  • We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units.[4]
  • Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs.[4]
  • Recurrent Neural networks are recurring over time.[5]
  • In this paper, we study the ability of RNN for hyperspectral data classification by extracting the contextual information from the data.[6]
  • We have introduced the basics of RNNs, which can better handle sequence data.[7]
  • Specifically, gated RNNs are much more common in practice.[7]
  • Furthermore, we will expand the RNN architecture with a single undirectional hidden layer that has been discussed so far.[7]
  • RNNs and LSTMs are special neural network architectures that are able to process sequential data, data where chronological ordering matters.[8]
  • LSTMs are essentially improved versions of RNNs, capable of interpreting longer sequences of data.[8]
  • The “Recurrent” portion of the RNN name comes from the fact that the input and outputs loop.[8]
  • The result of this architecture is that RNNs are capable fo handling sequential data.[8]
  • An RNN will not require linearity or model order checking.[9]
  • A recurrent neural network (RNN) is any network whose neurons send feedback signals to each other.[10]
  • A number of reviews already exist of some types of RNNs.[10]
  • To complement these contributions, the present summary focuses on biological recurrent neural networks (bRNN) that are found in the brain.[10]
  • To solve the noise-saturation dilemma in a RNN, excitatory feedback signals need to be balanced by inhibitory feedback signals.[10]
  • The proposed framework introduces only 1 additional parameter to establish the equivalence between rate and spiking RNN models.[11]
  • To this end, we have carefully designed our continuous rate RNNs to include several biological features.[11]
  • For constructing spiking RNNs, recent studies have proposed methods that built on the FORCE method to train spiking RNNs (8, 20⇓–22).[11]
  • (21) also relies on mapping a trained continuous-variable rate RNN to a spiking RNN model.[11]
  • In this study, we propose single-pixel imaging based on a recurrent neural network.[12]
  • An RNN is a network for handling time-series data since it can consider previous input data.[12]
  • The information of the reconstructed image in the RNN is accumulated and updated, as a new block is entered.[12]
  • An RNN is a type of neural network that can efficiently handle time-series data due to its recursive structure, as illustrated in Fig.[12]
  • We can construct a multi-layer recurrent neural network by stacking layers of RNN together.[13]
  • However, in general RNN does not go very deep due to the exploding gradient problem from long sequence of data.[13]
  • Thus RNN came into existence, which solved this issue with the help of a Hidden Layer.[14]
  • RNN have a “memory” which remembers all information about what has been calculated.[14]
  • An RNN remembers each and every information through time.[14]
  • Training an RNN is a very difficult task.[14]
  • On the other hand, RNNs do not consume all the input data at once.[15]
  • At each step, the RNN does a series of calculations before producing an output.[15]
  • You might be wondering, which portion of the RNN do I extract my output from?[15]
  • This is where RNNs are really flexible and can adapt to your needs.[15]
  • The beauty of recurrent neural networks lies in their diversity of application.[16]
  • So RNNs can be used for mapping inputs to outputs of varying types, lengths and are fairly generalized in their application.[16]
  • Let’s take a character level RNN where we have a word “Hello”.[16]
  • At each state, the recurrent neural network would produce the output as well.[16]
  • Another use for recurrent neural networks that is related to natural language is speech recognition and transcription.[17]
  • But the use of recurrent neural networks is not limited to text and language processing.[17]
  • LSTM is a special type of RNN that has a much more complex structure and solves the vanishing gradient problem.[17]
  • In this work, we adopted convolutional RNN or ConvRNN for individual identification using resting-state fMRI data.[18]
  • It is well known that RNN is difficult to train properly, even though it is a powerful model for time series modeling.[18]
  • Figure 3 shows that ConvRNN is better than conventional RNN for the majority of the time windows.[18]
  • For a fair comparison with this work, another conventional RNN was applied without the temporal averaging layer.[18]
  • An EM based training algorithm for recurrent neural networks.[19]
  • An application of recurrent neural networks to discriminative keyword spotting.[19]
  • A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks.[19]
  • Labelling Unsegmented Sequence Data with Recurrent Neural Networks.[19]
  • A Recurrent Neural Network is a type of neural network that contains loops, allowing information to be stored within the network.[20]
  • In short, Recurrent Neural Networks use their reasoning from previous experiences to inform the upcoming events.[20]
  • Recurrent Neural Networks can be thought of as a series of networks linked together.[20]
  • An RNN can be designed to operate across sequences of vectors in the input, output, or both.[20]
  • Recurrent Neural Networks (RNNs) are popular models that have shown great promise in many NLP tasks.[21]
  • As part of the tutorial we will implement a recurrent neural network based language model.[21]
  • The idea behind RNNs is to make use of sequential information.[21]
  • Another way to think about RNNs is that they have a “memory” which captures information about what has been calculated so far.[21]
  • This happens with the help of a special kind of neural network called a Recurrent Neural Network.[22]
  • The nodes in different layers of the neural network are compressed to form a single layer of recurrent neural networks.[22]
  • An RNN can handle sequential data, accepting the current input data, and previously received inputs.[22]
  • This RNN takes a sequence of inputs and generates a single output.[22]
  • Recurrent neural networks are not appropriate for tabular datasets as you would see in a CSV file or spreadsheet.[23]
  • There’s something magical about Recurrent Neural Networks (RNNs).[24]
  • Input vectors are in red, output vectors are in blue and green vectors hold the RNN's state (more on this soon).[24]
  • From left to right: (1) Vanilla mode of processing without RNN, from fixed-sized input to fixed-sized output (e.g. image classification).[24]
  • an RNN reads a sentence in English and then outputs a sentence in French).[24]
  • We now discuss the connection between the dynamics in the RNN as described by Eqs.[25]
  • ( A ) Diagram of an RNN cell operating on a discrete input sequence and producing a discrete output sequence.[25]
  • Internal components of the RNN cell, consisting of trainable dense matrices W (h) , W (x) , and W (y) .[25]
  • In this section, we introduce the operation of an RNN and its connection to the dynamics of waves.[25]
  • A recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data.[26]
  • Like feedforward and convolutional neural networks (CNNs), recurrent neural networks utilize training data to learn.[26]
  • Let’s take an idiom, such as “feeling under the weather”, which is commonly used when someone is ill, to aid us in the explanation of RNNs.[26]
  • Through this process, RNNs tend to run into two problems, known as exploding gradients and vanishing gradients.[26]
  • By default, the output of a RNN layer contains a single vector per sample.[27]
  • This vector is the RNN cell output corresponding to the last timestep, containing information about the entire input sequence.[27]
  • In addition, a RNN layer can return its final internal state(s).[27]
  • The returned states can be used to resume the RNN execution later, or to initialize another RNN.[27]
  • Basic RNNs are a network of neuron-like nodes organized into successive layers.[28]
  • It requires stationary inputs and is thus not a general RNN, as it does not process sequences of patterns.[28]
  • A special case of recursive neural networks is the RNN whose structure corresponds to a linear chain.[28]
  • Each higher level RNN thus studies a compressed representation of the information in the RNN below.[28]
  • The schematic shows a representation of a recurrent neural network.[29]
  • With a structure like this the RNN model starts to care about the past and what is coming next.[29]
  • Lets look at one RNN unit and the functions governing the computation.[29]
  • a time-travel science fiction movie title but backpropagation through time is the algorithm by which you train RNNs.[29]
  • Applications of RNNs RNN models are mostly used in the fields of natural language processing and speech recognition.[30]
  • The vanishing and exploding gradient phenomena are often encountered in the context of RNNs.[30]
  • In order to remedy the vanishing gradient problem, specific gates are used in some types of RNNs and usually have a well-defined purpose.[30]

소스

  1. ↑ 1.0 1.1 1.2 1.3 A Guide to RNN: Understanding Recurrent Neural Networks and LSTM
  2. ↑ 2.0 2.1 2.2 Recurrent neural network
  3. ↑ 3.0 3.1 3.2 Gated Feedback Recurrent Neural Networks
  4. ↑ 4.0 4.1 Recurrent Neural Network Regularization – Google Research
  5. ↑ Recurrent vs Recursive Neural Networks: Which is better for NLP?
  6. ↑ Convolutional Recurrent Neural Networks forHyperspectral Data Classification
  7. ↑ 7.0 7.1 7.2 9. Modern Recurrent Neural Networks — Dive into Deep Learning 0.15.1 documentation
  8. ↑ 8.0 8.1 8.2 8.3 What are RNNs and LSTMs in Deep Learning?
  9. ↑ Recurrent Neural Networks (RNN): Deep Learning for Sequential Data
  10. ↑ 10.0 10.1 10.2 10.3 Recurrent neural networks
  11. ↑ 11.0 11.1 11.2 11.3 Simple framework for constructing functional spiking recurrent neural networks
  12. ↑ 12.0 12.1 12.2 12.3 Single-pixel imaging using a recurrent neural network combined with convolutional layers
  13. ↑ 13.0 13.1 Vanilla Recurrent Neural Network
  14. ↑ 14.0 14.1 14.2 14.3 Introduction to Recurrent Neural Network
  15. ↑ 15.0 15.1 15.2 15.3 Beginner’s Guide on Recurrent Neural Networks with PyTorch
  16. ↑ 16.0 16.1 16.2 16.3 Fundamentals Of Deep Learning
  17. ↑ 17.0 17.1 17.2 What are recurrent neural networks (RNN)?
  18. ↑ 18.0 18.1 18.2 18.3 Application of Convolutional Recurrent Neural Network for Individual Recognition Based on Resting State fMRI Data
  19. ↑ 19.0 19.1 19.2 19.3 RECURRENT NEURAL NETWORKS
  20. ↑ 20.0 20.1 20.2 20.3 Recurrent Neural Network
  21. ↑ 21.0 21.1 21.2 21.3 Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs
  22. ↑ 22.0 22.1 22.2 22.3 Recurrent Neural Network (RNN) Tutorial for Beginners
  23. ↑ When to Use MLP, CNN, and RNN Neural Networks
  24. ↑ 24.0 24.1 24.2 24.3 The Unreasonable Effectiveness of Recurrent Neural Networks
  25. ↑ 25.0 25.1 25.2 25.3 Wave physics as an analog recurrent neural network
  26. ↑ 26.0 26.1 26.2 26.3 What are Recurrent Neural Networks?
  27. ↑ 27.0 27.1 27.2 27.3 Recurrent Neural Networks (RNN) with Keras
  28. ↑ 28.0 28.1 28.2 28.3 Recurrent neural network
  29. ↑ 29.0 29.1 29.2 29.3 Understanding Recurrent Neural Networks in 6 Minutes
  30. ↑ 30.0 30.1 30.2 Recurrent Neural Networks Cheatsheet

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