RaisinLoaf69

3 ms·

1.
by RaisinLoaf69 5y ago
We’re not holding anything back, you should be able to recreate our findings from the paper. More broadly, there are a number of ways to recreate the network using the relatively simple learning rule we provide. In theory, this learning rul
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by RaisinLoaf69 5y ago
This is strictly not a neural network, so there is no backpropagation. Credit assignment is done on each edge using a local rule (Eg. using only its current state and the state of touching edges). To scale the network you just have to add m
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by RaisinLoaf69 5y ago
Since we have no global processor, each edge is changing using only local information, a lot of stuff that makes sense in our network doesn't really make sense in ANNs and vice versa. For example, in our newest paper ( https:/&#x2
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by RaisinLoaf69 5y ago
Sweet! The basic learning comparison (Vc-Vf) can be implemented in tons of physical systems (memristors, springs, water pipes). So seeing it in other mediums would be pretty cool.
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by RaisinLoaf69 5y ago
It is in fact just a digital potentiometer. In the experiment shown in the paper I linked we’re using a 128 position one. In new work we’ve actually shifted to using transistors which are better for a number of reasons (smaller, faster, non
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by RaisinLoaf69 5y ago
Thank you! Definitely a group effort. Two big things that contributed to the choice: We knew we didn’t want the system to have to store memory on each edge so that eliminated a lot of our options. In our experimental learning rule we only h
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by RaisinLoaf69 5y ago
I actually work on this project! Feel free to ask any questions. Also, here’s an arxiv link to one of the papers if anyone is interested: https://arxiv.org/abs/2108.00275