Difference between revisions of "Transformer"
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*[[Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)]] | *[[Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)]] | ||
*[[Autoencoders / Encoder-Decoders]] | *[[Autoencoders / Encoder-Decoders]] | ||
| − | *[[Natural Language Processing (NLP | + | *[[Natural Language Processing (NLP)]] |
“Attend” to specific parts of the input (an image or text) in sequence, one after another. By relying on a sequence of glances, they capture (visual) structure, can be contrasted with other (machine vision) techniques that process a whole input e.g. image in a single, forward pass. | “Attend” to specific parts of the input (an image or text) in sequence, one after another. By relying on a sequence of glances, they capture (visual) structure, can be contrasted with other (machine vision) techniques that process a whole input e.g. image in a single, forward pass. | ||
Revision as of 06:53, 5 January 2019
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- Sequence to Sequence (Seq2Seq)
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
- Autoencoders / Encoder-Decoders
- Natural Language Processing (NLP)
“Attend” to specific parts of the input (an image or text) in sequence, one after another. By relying on a sequence of glances, they capture (visual) structure, can be contrasted with other (machine vision) techniques that process a whole input e.g. image in a single, forward pass.
