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Releasing illustrative colab for ensemble_loss_landscape.

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Clara Huiyi Hu
2020-12-10 19:52:32 +00:00
committed by Louise Deason
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README.md
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@@ -59,6 +59,8 @@ https://deepmind.com/research/publications/
* [Graph Matching Networks for Learning the Similarity of Graph Structured * [Graph Matching Networks for Learning the Similarity of Graph Structured
Objects](graph_matching_networks), ICML 2019 Objects](graph_matching_networks), ICML 2019
* [REGAL: Transfer Learning for Fast Optimization of Computation Graphs](regal) * [REGAL: Transfer Learning for Fast Optimization of Computation Graphs](regal)
* [Deep Ensembles: A Loss Landscape Perspective](ensemble_loss_landscape)
ensemble_loss_landscape/README.md
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# Accompanying code for Deep Ensemble: A Loss Landscape Perspective
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/ensemble_loss_landscape/cifar10_medium_cnn_experiments.ipynb)
The Colab notebook `cifar10_medium_cnn_experiments.ipynb` illustrates the CIFAR-10
experiments in the paper:
[Deep Ensembles: A Loss Landscape Perspective](https://arxiv.org/abs/1912.02757)
by Stanislav Fort, Huiyi Hu and Balaji Lakshminarayanan
These experiments investigate the effects of ensembling and variational Bayesian
methods, please see the paper for more details.
ensemble_loss_landscape/Release_cifar10_medium_cnn_experiments.ipynb → ensemble_loss_landscape/cifar10_medium_cnn_experiments.ipynb
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