# 순환 인공 신경망

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## 노트

- 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

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

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