Aug 02, 2017

The kind of math that would be necessary to understand papers like [1][2][3][4].

[1]: https://arxiv.org/abs/1703.04933 [2]: https://arxiv.org/abs/1701.07875 [3]: https://arxiv.org/abs/1706.01350 [4]: https://arxiv.org/abs/1512.04860

May 06, 2017

It's not so much that there's just one or two specific results, but rather that there's just far more researchers working on this now, and quite a few are working on more theoretical stuff. Sometimes, that theory results in really practical outcomes - a good example would be https://arxiv.org/abs/1701.07875 . Or another by the same researcher: https://arxiv.org/abs/1506.00059 .

We've seen, in the last year or two, interesting results in nearly every area of theoretical research of deep learning, including generalization, optimization, generative modelling, bayesian models, and network architecture.