What's the current state-of-the-art in machine learning on complex data? To clarify what I mean: most machine learning algorithms assume you're dealing with a small set of scalar variables.
There have been good results from e.g. tiled neural networks for image processing, where, very roughly speaking, you train a network to operate on a small group of adjacent pixels and then tile it across the image. (This has to be more or less how animal brains process vision.) But for data which is neither scalar nor straightforwardly tiled, like program code or natural language text? It's been a good while since I looked into the state-of-the-art in machine learning; where are things at nowadays with that kind of data? ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
