Difference between revisions of "Generative AI"
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* [[Natural Language Generation (NLG)]] | * [[Natural Language Generation (NLG)]] | ||
* [http://en.wikipedia.org/wiki/Generative_model Generative model | Wikipedia] | * [http://en.wikipedia.org/wiki/Generative_model Generative model | Wikipedia] | ||
| − | * [[Data Augmentation# | + | * [[Data Augmentation#Synthetic Labeling|Synthetic Labeling]] |
* Adversarial | * Adversarial | ||
** [[Conditional Adversarial Architecture (CAA)]] | ** [[Conditional Adversarial Architecture (CAA)]] | ||
Revision as of 06:37, 21 August 2019
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- machines learn to perceive their surroundings by training only on data obtained by themselves
- AI Solver
- Generative Query Network (GQN)
- Discriminative vs. Generative
- Natural Language Generation (NLG)
- Generative model | Wikipedia
- Synthetic Labeling
- Adversarial
Generative modeling asks how likely it is, given condition X, that you’ll observe outcome Y. The approach has proved incredibly potent and versatile. How Artificial Intelligence Is Changing Science | Dan Falk - Quanta Magazine
Deep Generative Modeling
Agents
Generative Modeling Language
