Difference between revisions of "Transformer-XL"
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* [[Memory Networks]] | * [[Memory Networks]] | ||
* [[Attention Mechanism/Model - Transformer Model]] | * [[Attention Mechanism/Model - Transformer Model]] | ||
| + | * [[Autoencoder (AE) / Encoder-Decoder]] | ||
Revision as of 16:11, 19 January 2019
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- A Light Introduction to Transformer-XL | Elvis - Medium
- Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
- Natural Language Processing (NLP)
- Memory Networks
- Attention Mechanism/Model - Transformer Model
- Autoencoder (AE) / Encoder-Decoder
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
