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 Inference (NLI) and Recognizing Textual Entailment (RTE)]] | + | *[[Natural Language Processing (NLP), Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)]] |
“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 08:23, 26 August 2018
- Sequence to Sequence (Seq2Seq)
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
- Autoencoders / Encoder-Decoders
- Natural Language Processing (NLP), Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)
“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.
