Looking at your posts, there’s no hooks or trying to sell your work, which is a shame cause LSRDR’s seem useful. Since they are you useful, you should be able to show it.
For example, you trained an LSRDR for text embedding, which you could show at the beginning of the post. Then showing the cool properties of pseudo-determinism & lack of noise compared to NN’s. THEN all the maths. So the math folks know if the post is worth their time, and the non-math folks can upvote and share with their mathy friends.
I am assuming that you care about [engagement, useful feedback, connections to other work, possible collaborators] here. If not, then sorry for the unwanted advice!
I’m still a little fuzzy on your work, but possible related papers that come to mind are on tensor networks.
Compositionality Unlocks Deep Interpretable Models—they efficiently train tensor networks on [harder MNIST], showing approximately equivalent loss to NN’s, and show the inherent interpretability in their model.
Tensorization is [Cool essentially] - https://arxiv.org/pdf/2505.20132 - mostly a position and theoretical paper arguing why tensorization is great and what limitations.
Im pretty sure both sets of authors here read LW as well.
I would have thought that a fitness function that is maximized using something other than gradient ascent and which can solve NP-complete problems at least in the average case would be worth reading since that means that it can perform well on some tasks but it also behaves mathematically in a way that is needed for interpretability. The quality of the content is inversely proportional to the number of views since people don’t think the same way as I do.
And I really do not want to collaborate with people who are not willing to read the post. This is especially true of people in academia since universities promote violence and refuse to acknowledge any wrongdoing. Universities are the absolute worst.
Instead of engaging with the actual topic, people tend to just criticize stupid stuff simply because they only want to read about what they already know or what is recommended by their buddies; that is a very good way not to learn anything new or insightful. For this reason, even the simplest concepts are lost on most people.
That does clarify a lot of things for me, thanks!
Looking at your posts, there’s no hooks or trying to sell your work, which is a shame cause LSRDR’s seem useful. Since they are you useful, you should be able to show it.
For example, you trained an LSRDR for text embedding, which you could show at the beginning of the post. Then showing the cool properties of pseudo-determinism & lack of noise compared to NN’s. THEN all the maths. So the math folks know if the post is worth their time, and the non-math folks can upvote and share with their mathy friends.
I am assuming that you care about [engagement, useful feedback, connections to other work, possible collaborators] here. If not, then sorry for the unwanted advice!
I’m still a little fuzzy on your work, but possible related papers that come to mind are on tensor networks.
Compositionality Unlocks Deep Interpretable Models—they efficiently train tensor networks on [harder MNIST], showing approximately equivalent loss to NN’s, and show the inherent interpretability in their model.
Tensorization is [Cool essentially] - https://arxiv.org/pdf/2505.20132 - mostly a position and theoretical paper arguing why tensorization is great and what limitations.
Im pretty sure both sets of authors here read LW as well.
I would have thought that a fitness function that is maximized using something other than gradient ascent and which can solve NP-complete problems at least in the average case would be worth reading since that means that it can perform well on some tasks but it also behaves mathematically in a way that is needed for interpretability. The quality of the content is inversely proportional to the number of views since people don’t think the same way as I do.
Wheels on the Bus | @CoComelon Nursery Rhymes & Kids Songs
Stuff that is popular is usually garbage.
But here is my post about the word embedding.
Interpreting a matrix-valued word embedding with a mathematically proven characterization of all optima — LessWrong
And I really do not want to collaborate with people who are not willing to read the post. This is especially true of people in academia since universities promote violence and refuse to acknowledge any wrongdoing. Universities are the absolute worst.
Instead of engaging with the actual topic, people tend to just criticize stupid stuff simply because they only want to read about what they already know or what is recommended by their buddies; that is a very good way not to learn anything new or insightful. For this reason, even the simplest concepts are lost on most people.