Difference between revisions of "Generative AI"
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* [http://en.wikipedia.org/wiki/Generative_model Generative model | Wikipedia] | * [http://en.wikipedia.org/wiki/Generative_model Generative model | Wikipedia] | ||
* [[Data Augmentation#Synthetic Labeling|Synthetic Labeling]] | * [[Data Augmentation#Synthetic Labeling|Synthetic Labeling]] | ||
| − | * Adversarial | + | * Adversarial Networks |
** [[Conditional Adversarial Architecture (CAA)]] | ** [[Conditional Adversarial Architecture (CAA)]] | ||
** [[Generative Adversarial Network (GAN)]] | ** [[Generative Adversarial Network (GAN)]] | ||
Revision as of 06:50, 2 October 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 Networks
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
