Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
Abstract
We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning....
Code References
tensorflow/tensorflow
4 files
tensorflow/python/keras/optimizer_v2/adagrad.py
1
- [Duchi et al., 2011](
tensorflow/python/training/adagrad_da.py
1
:[Duchi et al., 2011](http://jmlr.org/papers/v12/duchi11a.html)
tensorflow/python/training/adagrad.py
1
:[Duchi et al., 2011](http://jmlr.org/papers/v12/duchi11a.html)
tensorflow/python/training/proximal_adagrad.py
1
[Duchi et al., 2011](http://jmlr.org/papers/v12/duchi11a.html)
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