3 ms·

Another paper related to AD was posted last month: The Simple Essence of Automatic Differentiation [0]. Video presentation [1]. I found it pretty awesome, just

Another paper related to AD was posted last month: The Simple Essence of Automatic Differentiation [0]. Video presentation [1]. I found it pretty awesome, just in case you hadn't seen it. [0]: https//news.ycombinator.com/item?id=18306860 [1]: https://www.youtube.com/watch?v=ne99laPUxN4 https://www.youtube.com/watch?v=ne99laPUxN4

YorkshireSeason 8y ago
Also not mentioned in the Baydin et al paper (which has been knocking about for a while: it was submitted in 2017, so not surprising): * F. Wang, X. Wu, G. Essertel, J. Decker, T. Rompf, Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator, see [1], and [4] for a presentation based on this paper. * S. Laue, M. Mitterreiter, J. Giesen, Computing Higher Order Derivatives of Matrix and Tensor Expressions, see [2], discussed in [3]. Quite a bit of exciting work in better understanding backpropagation going on right now. [1] https://arxiv.org/abs/1803.10228 https://arxiv.org/abs/1803.10228 [2] http://www.matrixcalculus.org/matrixcalculus.pdf http://www.matrixcalculus.org/matrixcalculus.pdf [3] https://news.ycombinator.com/item?id=18464003 https://news.ycombinator.com/item?id=18464003 [4] https://www.youtube.com/watch?v=igRLKYgpHy0 https://www.youtube.com/watch?v=igRLKYgpHy0