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N
ncbi.nlm.nih.gov
official
https://www.ncbi.nlm.nih.gov/books/NBK597502/
Recurrent neural network (RNN) is a specialized neural network with feedback connection for processing sequential data or time-series data in which the output obtained is fed back into it as input along with the new input at every time step. In 1997, one of the most popular RNN architectures, the long short-term memory (LSTM) network which can process long sequences, was proposed. proposed a novel solution composed of GRU-RNN layers with attention mechanism by including switching decoder in their abstractive summarizer architecture [28] where the text generator module has a switch which can enable the module to choose between two options: (1) generate a word from the vocabulary and (2) point to one of the words in the input text. In 2014, many-to-many RNN-based encoder–decoder architecture was proposed where one RNN encodes the input sequence of text to a fixed-length vector representation, while another RNN decodes the fixed-length vector to the target translated sequence [30].
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scispace.com
article
https://scispace.com/pdf/extensions-of-recurrent-neural-network-language-mode…
Model PPL KN5 141 Random forest (Peng Xu) [8] 132 Structured LM (Filimonov) [9] 125 Syntactic NN LM (Emami) [10] 107 RNN trained by BP 113 RNN trained by BPTT 106 4x RNN trained by BPTT (mixture) 98 where f(z) and g(z) are sigmoid and softmax activation functions (the softmax function in the output layer is used to make sure that the outputs form a valid probability distribution, i.e. all outputs are greater than 0 and their sum is 1): f(z) = 1 1 + e−z , g(zm) = ezm P k ezk (4) The cross entropy criterion is used to obtain an error vector in the output layer, which is then backpropagated to the hidden layer. Table 2 shows comparison of the feedforward [12], simple recur-rent [4] and BPTT-trained recurrent neural network language models on two corpora. The comparison to standard feedforward neural network based language models, as well as comparison to BP trained RNN mod-els shows clearly the potential of the presented model.
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labellerr.com
article
https://www.labellerr.com/blog/evolution-of-neural-networks-to-large-language…
Explore the evolution from neural networks to large language models, highlighting key advancements in NLP with the rise of transformer models. Neural network-based language models have revolutionized natural language processing (NLP) by enabling computers to predict and generate text with remarkable accuracy. Initial models like n-grams and Hidden Markov Models laid the foundation, but their limitations prompted the development of neural networks, including Recurrent Neural Networks (RNNs) and advanced versions like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). The Transformer architecture, introduced in the "Attention is All You Need" paper, has revolutionized NLP by leveraging attention mechanisms to process sequences in parallel, rather than sequentially as in previous models like RNNs or LSTMs. Before transformers, recurrent neural networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, were the go-to models for sequential tasks. The introduction of encoder-decoder architectures and the Transformer model revolutionized language processing, enabling improved handling of sequential data and attention mechanisms.
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slds-lmu.github.io
article
https://slds-lmu.github.io/seminar_nlp_ss20/recurrent-neural-networks-and-the…
Considering a recurrent neural network with one hidden layer that is used to predict words or characters, the output should be discrete and the model maps input sequence to output sequence of the same length. Since \(W\_{hy}\) is shared across all time sequence, the total loss w.r.t. the weight matrix connecting hidden states to outputs is simply a sum of single losses:. A candidate hidden state \(\tilde{h}^{(t)}\) is then obtained in the following three steps: 1) weight matrix \(W\_{xh}\) is multiplied by current input \(x^{(t)}\); 2) weight matrix \(W\_{hh}\) is multiplied by the element-wise product (denoted as \(\odot\)) of reset gate \(r^{(t)}\) and previous hidden state \(h^{(t)}\); 3) both products are added and a \(tanh\) function is applied in order to output the candidate values for a hidden state. One can add shortcut connections to provide shorter paths for gradients, such networks are referred to as DT(S)-RNNs. If deep transitions with shortcuts are implemented both in hidden and output layers, the resulting model is called DOT(S)-RNNs. Pascanu et al.
M
microsoft.com
research
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/RNN4LU.pdf
In common with other continuous space language models such as feed-forward neural network LMs [7, 8, 9, 10, 11] and the Hierarchical Log-Bilinear model [12], the RNN-LM represents each word as a high-dimensional real-valued vector. The values in the hidden and output layers are computed as fol-lows: s(t) = f (Uw(t) + Ws(t−1)) (1) y(t) = g (Vs(t)) , (2) Figure 1: Recurrent Neural Network Model for Language Un-derstanding. To obtain the continu-ous space embedding of words for use in the side-channel, we trained a standard RNN-LU model with a hidden layer size of 200, and used the input layer weights as the word representa-tions [13]. As a second experiment, we trained RNN-LU systems us-ing three different embeddings to represent the future words as in Section 2.2: a task-specific embedding learned from training an RNN-LU system on the ATIS data; a language-model (RNN-LM) embedding also trained with the ATIS data; and finally the generic SENNA embeddings derived from the Wikipedia cor-pus [38].
R
researchgate.net
research
https://www.researchgate.net/publication/399856665_Recurrent_Neural_Network_M…
This paper presents an overview of RNN-based models applied to core NLP tasks such as language modeling, text classification, sentiment analysis
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ibm.com
article
https://www.ibm.com/think/topics/recurrent-neural-networks
A recurrent neural network or RNN is a deep [neural network](https://www.ibm.com/topics/neural-networks) trained on sequential or time series data to create a [machine learning (ML)](https://www.ibm.com/topics/machine-learning) model that can make sequential predictions or conclusions based on sequential inputs. Like traditional neural networks, such as feedforward neural networks and [convolutional neural networks (CNNs)](https://www.ibm.com/topics/convolutional-neural-networks), recurrent neural networks use training data to learn. While traditional [deep learning](https://www.ibm.com/topics/deep-learning) networks assume that inputs and outputs are independent of each other, the output of recurrent neural networks depend on the prior elements within the sequence. The principles of BPTT are the same as traditional [backpropagation](https://www.ibm.com/think/topics/backpropagation), where the model trains itself by calculating errors from its output layer to its input layer. ### Standard RNNs. The most basic version of an RNN, where the output at each time step depends on both the current input and the hidden state from the previous time step, suffers from problems such as vanishing gradients, making it difficult for them to learn long-term dependencies.
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medium.com
article
https://medium.com/@harsuminder/from-rnns-to-llms-a-journey-through-sequentia…
This is the story of how these innovations shaped the landscape of natural language processing (NLP), paving the way for today's Large Language Models (LLMs).