Difference between revisions of "AdaNet"
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* [[Automated Machine Learning (AML) - AutoML]] | * [[Automated Machine Learning (AML) - AutoML]] | ||
* [[Self Learning Artificial Intelligence - AutoML & World Models]] | * [[Self Learning Artificial Intelligence - AutoML & World Models]] | ||
| + | * [[Reinforcement Learning (RL)]] | ||
a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on our recent reinforcement learning and evolutionary-based AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models. | a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on our recent reinforcement learning and evolutionary-based AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models. | ||
Revision as of 22:24, 24 February 2019
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- AdaNet
- Automated Machine Learning (AML) - AutoML
- Self Learning Artificial Intelligence - AutoML & World Models
- Reinforcement Learning (RL)
a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on our recent reinforcement learning and evolutionary-based AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models. AdaNet is easy to use, and creates high-quality models, saving ML practitioners the time normally spent selecting optimal neural network architectures, implementing an adaptive algorithm for learning a neural architecture as an ensemble of subnetworks. AdaNet is capable of adding subnetworks of different depths and widths to create a diverse ensemble, and trade off performance improvement with the number of parameters. Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees | Charles Weill
