Difference between revisions of "Transformer-XL"

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[http://www.youtube.com/results?search_query=Transformer+XL+attention+model+ai+deep+learning+model YouTube search...]
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|title=PRIMO.ai
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[http://www.youtube.com/results?search_query=Transformer-XL+attention+model+ai+deep+learning+model YouTube search...]
 
[http://www.google.com/search?q=Transformer+XL+attention+model+deep+machine+learning+ML ...Google search]
 
[http://www.google.com/search?q=Transformer+XL+attention+model+deep+machine+learning+ML ...Google search]
  
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* [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]]
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* [[Attention]] Mechanism  ...[[Transformer]] ...[[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
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* [[Natural Language Processing (NLP)]]
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* [http://medium.com/dair-ai/a-light-introduction-to-transformer-xl-be5737feb13 A Light Introduction to Transformer-XL | Elvis - Medium]
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* [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 | Rani Horev - Towards Data Science]
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* [http://openreview.net/forum?id=HJePno0cYm Transformer-XL: Language Modeling with Longer-Term Dependency | Z. Dai, Z. Yang, Y. Yang, W.W. Cohen, J. Carbonell, Quoc V. Le, ad R. Salakhutdinov]
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* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 
* [[Memory Networks]]
 
* [[Memory Networks]]
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* [[Attention Mechanism/Model - Transformer Model]]
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* [[Autoencoder (AE) / Encoder-Decoder]]
  
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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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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]
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Combines the two leading architectures for language modeling:
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# [[Recurrent Neural Network (RNN)]] to handles the input tokens — words or characters — one by one to learn the relationship between them
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# [[Attention]] Mechanism/[[Transformer]] Model to receive a segment of tokens and learns the dependencies between at once them using an attention mechanism.  
  
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[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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http://skymind.ai/images/wiki/attention_mechanism.png
 
  
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Latest revision as of 12:38, 3 May 2023

YouTube search... ...Google search


Combines the two leading architectures for language modeling:

  1. Recurrent Neural Network (RNN) to handles the input tokens — words or characters — one by one to learn the relationship between them
  2. Attention Mechanism/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


0*mrV1VMF_G2mhQ9Jj.png


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