the challenges to deep learning just keep on coming. here’s an important technical paper by Sengupta and Friston showing that accuracy doesn’t always entail robustness. (corroborating some of the issues on my own critical appraisal of #deeplearning.) https://arxiv.org/abs/1804.11313
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Gary, debunking DL myths is healthy for the ecosystem, and your contribution is helpful, to a degree. But, beyond debunking, do you have anything constructive to offer?
Sure, e.g. I make specific suggestions about innateness and aspects of symbolic architectures that I believe need to be incorporated in ML, in both of my January arXiv articles
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Care to link them here?
Thanks. Twitter will use this to make your timeline better. UndoUndo
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