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Update README.md - Google Vizier paper. #17

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Expand Up @@ -184,7 +184,7 @@ by active learning (by developers of Spacy), text and image

* Platforms:
* [RayTune](http://tune.io/): Ray Tune is a Python library for hyperparameter tuning at any scale (with a focus on deep learning and deep reinforcement learning). Supports any machine learning framework, including PyTorch, XGBoost, MXNet, and Keras.
* [Katib](https://github.com/kubeflow/katib): Kubernete's Native System for Hyperparameter Tuning and Neural Architecture Search, inspired by [Google vizier](https://static.googleusercontent.com/media/ research.google.com/ja//pubs/archive/ bcb15507f4b52991a0783013df4222240e942381.pdf) and supports multiple ML/DL frameworks (e.g. TensorFlow, MXNet, and PyTorch).
* [Katib](https://github.com/kubeflow/katib): Kubernete's Native System for Hyperparameter Tuning and Neural Architecture Search, inspired by [Google vizier](https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/bcb15507f4b52991a0783013df4222240e942381.pdf) and supports multiple ML/DL frameworks (e.g. TensorFlow, MXNet, and PyTorch).
* [Hyperas](https://maxpumperla.com/hyperas/): a simple wrapper around hyperopt for Keras, with a simple template notation to define hyper-parameter ranges to tune.
* [SIGOPT](https://sigopt.com/): a scalable, enterprise-grade optimization platform
* [Sweeps](https://docs.wandb.com/library/sweeps) from [Weights & Biases] (https://www.wandb.com/): Parameters are not explicitly specified by a developer. Instead they are approximated and learned by a machine learning model.
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