Difference between revisions of "Transformer-XL"

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* [[Attention Mechanism/Model - Transformer Model]]
 
* [[Attention Mechanism/Model - Transformer Model]]
  
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Attention mechanisms in neural networks are about memory access. That’s the first thing to remember about attention: it’s something of a misnomer. [http://skymind.ai/wiki/attention-mechanism-memory-network A Beginner's Guide to Attention Mechanisms and Memory Networks | Skymind]
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combines the two leading architectures for language modeling — [1] [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]] to handles the input tokens — words or characters — one by one to learn the relationship between them, and [2] [[Attention Mechanism/Model - Transformer Model]] to receive a segment of tokens and learns the dependencies between at once them using an attention mechanism. [http://towardsdatascience.com/transformer-xl-explained-combining-transformers-and-rnns-into-a-state-of-the-art-language-model-c0cfe9e5a924 Transformer-XL Explained: Combining Transformers and RNNs into a State-of-the-art Language Model; Summary of “Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context” | Rani Horev - Towards Data Science]
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Transformer Model - The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an [[Autoencoder (AE) / Encoder-Decoder]] configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. [http://arxiv.org/abs/1706.03762  Attention Is All You Need | A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin]
 
  
  

Revision as of 16:03, 19 January 2019

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combines the two leading architectures for language modeling — [1] Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN) to handles the input tokens — words or characters — one by one to learn the relationship between them, and [2] Attention Mechanism/Model - Transformer Model to receive a segment of tokens and learns the dependencies between at once them using an attention mechanism. Transformer-XL Explained: Combining Transformers and RNNs into a State-of-the-art Language Model; Summary of “Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context” | Rani Horev - Towards Data Science


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