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Code for Global Convergence of Block Coordinate Descent in Deep Learning (ICML 2019)
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README.md
bcd_dnn_mlp_mnist.ipynb
bcd_dnn_mlp_mnist_deep_output.ipynb
plot.ipynb
sgd_dnn_mlp_mnist_deep_keras.ipynb

README.md

BCD-for-DNNs-PyTorch

Implementation of BCD for DNNs in Global Convergence of Block Coordinate Descent in Deep Learning (Zeng et al., 2019) in PyTorch with the MNIST dataset (see also T. T.-K. Lau et al., 2018).

bcd_dnn_mlp_mnist.ipynb: 3-layer MLP, MNIST, BCD (PyTorch)

bcd_dnn_mlp_mnist_deep.ipynb: 10-layer MLP, MNIST, BCD (PyTorch)

sgd_dnn_mlp_mnist_deep_keras.ipynb: 10-layer MLP, MNIST, SGD (TensorFlow and Keras; adapted from this repo)

plot.ipynb: Produces plots in the paper


J. Zeng*, T. T.-K. Lau*, S. Lin and Y. Yao (2019). Global Convergence of Block Coordinate Descent in Deep Learning. In Proceedings of the 36th International Conference on Machine Learning (ICML).

*Equal contribution

T. T.-K. Lau, J. Zeng, B. Wu and Y. Yao (2018). A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training. In International Conference on Learning Representations (ICLR), Workshop Track.

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