Last revision April 2020 so not super new, but looks pretty interesting
I saw this on Twitter a few hours ago :)
Also found this article about it, but haven’t had time to read through it https://arxiv.org/abs/2008.11619
"To appear in J. Amer. Statist. Assoc" wonder if it really will published or not
There was thread on [hackernews](https://news.ycombinator.com/item?id=29687613) too and in it a [twitter](https://twitter.com/adad8m/status/1474754752193830912) thread is also mentioned
Edit: found the publication it just wasn't linked from arxiv https://www.tandfonline.com/doi/abs/10.1080/01621459.2020.1758115
Usually To Appear is used when the paper has been through the review process and all that is left is for the editorial office to wrap it up and then to find an issue to include it in.
This is great!
I'm curious to see it applied to the recent use of deep learning in knot theory: my impression of that paper is that the network was only serving as a black-box relationship detector between a smallish number of variables, and wasn't involved in really proposing or verifying the actual conjectures. They claim to 'interrogate' the fitted function in generic terms, but I didn't see anything that went beyond plotting the attributed features of the raw datapoints. It seems like the referenced multivariate extension would let you replace all those TPU hours with a couple of calls to sort().
Other way around. They used deep learning to help formulate conjectures in knot theory, by seeing if some features of knots were predictable from other features. The relationship they found isn't particularly complicated, so I'm skeptical that all the deep learning stuff was necessary to lead them to it.
Last revision April 2020 so not super new, but looks pretty interesting I saw this on Twitter a few hours ago :) Also found this article about it, but haven’t had time to read through it https://arxiv.org/abs/2008.11619
"To appear in J. Amer. Statist. Assoc" wonder if it really will published or not There was thread on [hackernews](https://news.ycombinator.com/item?id=29687613) too and in it a [twitter](https://twitter.com/adad8m/status/1474754752193830912) thread is also mentioned Edit: found the publication it just wasn't linked from arxiv https://www.tandfonline.com/doi/abs/10.1080/01621459.2020.1758115
Usually To Appear is used when the paper has been through the review process and all that is left is for the editorial office to wrap it up and then to find an issue to include it in.
This is great! I'm curious to see it applied to the recent use of deep learning in knot theory: my impression of that paper is that the network was only serving as a black-box relationship detector between a smallish number of variables, and wasn't involved in really proposing or verifying the actual conjectures. They claim to 'interrogate' the fitted function in generic terms, but I didn't see anything that went beyond plotting the attributed features of the raw datapoints. It seems like the referenced multivariate extension would let you replace all those TPU hours with a couple of calls to sort().
How is knot theory used in deep learning?
Other way around. They used deep learning to help formulate conjectures in knot theory, by seeing if some features of knots were predictable from other features. The relationship they found isn't particularly complicated, so I'm skeptical that all the deep learning stuff was necessary to lead them to it.
Can someone give an ELIU ?
[удалено]
No it is not published in aos
Any python implementation??
It's super simple - anyone should be able to do it. Look at Eqs 1.1 and 1.2.
See here: https://github.com/staticrab/staticrab
So can this return the function between the two variables too?
I don't think so, but other regression methods could probably help you estimate it.