I'm curious if anyone has applied this idea in their Go software, and what results you obtained? It is a way to make rotations (and transpositions with more effort) go away as an issue, regardless of the way you input the board you'd get the same result back out. Short summary from the paper ( https://arxiv.org/pdf/1602.02660.pdf):
We have introduced a framework for building rotation equivariant neural networks, using four new layers which can easily be inserted into existing network architectures. Beyond adapting the minibatch size used for training, no further modifications are required. We demonstrated improved performance of the resulting equivariant networks on datasets which exhibit full rotational symmetry, while reducing the number of parameters. A fast GPU implementation of the rolling operation for Theano (using CUDA kernels) is available at https://github.com/benanne/kaggle-ndsb. It was apparently used by this science competition winner: http://benanne.github.io/2015/03/17/plankton.html And there's related codebase here that implements the paper Group Equivariant Convolutional Networks ( https://tacocohen.files.wordpress.com/2016/06/gcnn.pdf). https://github.com/tscohen/gconv_experiments The paper makes it sound like implementing for rotation would be straight forward, and implementing for transposition more difficult but also doable.Which sounds perfect for Go AI applications. -Jonathan
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