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I'm working in the lab that built Spaun [1] that might be of interest to you. We use the Vector Symbolic Architectures you talk about in combination with Deep L
by Seanny123 10y ago
I'm working in the lab that built Spaun [1] that might be of interest to you. We use the Vector Symbolic Architectures you talk about in combination with Deep Learning and other stuff to build brain models.
As far I know, my lab is one of the few places mixing symbolic learning with Deep Learning. Do you or anyone else know other modern attempts (Reinforcement Learning?) with this approach?
[1] http://science.sciencemag.org/content/338/6111/1202 http://science.sciencemag.org/content/338/6111/1202
- psygnisfive 10y agoI know of no other work like this, unfortunately. But I would caution against saying this is a Vector Symbolic Architecture; the representations are, as far as I know, quite distinct.
- deleted 10y ago[deleted]
- Eridrus 10y agoIt took a while to figure out which group you're talking about, but here is some recent work from another group: https://arxiv.org/abs/1609.05518 https://arxiv.org/abs/1609.05518
- psygnisfive 10y agoOh! Was Murray Shanahan's lab involved with this? Cool. I've chatted with Murray a very tiny bit on twitter about this all. That paper is part of what inspired me to write this blog post, because I was dissatisfied with the approach they propose. It felt to me like they had found a way to restrict "symbolic" data to a very narrow domain where issues like hierarchical structure was totally absent, and vectorial representation was therefore quite straight forward, and I wanted to figure out an alternative. Instead of restricting the kinds of symbolic data involved, do the full spectrum. I had the symbolic vector idea in my head for about two years as an answer to the representation problem, but I hadn't seen what the right answer was to the other aspects, at least not clearly. But that paper motivated me to start thinking about it again, and this time I saw the answer. :)