Wasserstein geometry = quotient geometry of permutation invariance.
In this blog, I explain why this is the natural language for exchangeable particles—and why mean-field neural network training shows up as a W2 gradient flow.
mufan-li.github.io/OT2/
Introduce yourself with five concerts you’ve seen
Linkin Park
Dream Theater
X-Japan
Lamb of God
Rivers of Nihil
Thank you!
I think the award has its fair place, since it’s hard to predict the value and impact of a paper 10 years later. I just think we shouldn’t devalue a paper based on current popularity alone, given that’s more about sociology rather than the work itself.
I think this rise and fall of popularity of methods says more about the community than the method itself. Honestly who knows if there’s just one clever trick needed to make GANs better than diffusion models.
We really don’t need to be so obsessed about SOTA.
May I be added as well?
People should write more blog posts. I have been pleasantly surprised over and over again by how much people found them beneficial, when I just wanted to share some cool math.
I don’t think “surprising” is a meaningful quality to evaluate. Most results are trivial once understood properly, and surprise mostly depends existing intuition being inadequate.
E.g. scaling laws are basically non-parametric rates, and you deviate if you are not minimax optimal.
Nice to know there are other metal heads here :)
Starter packs are helpful as well as the twitter import tool chromewebstore.google.com/detail/sky-f...
Perhaps if the users with higher connectivity (influencers) post more on B than X, then the majority of users who are just consuming the content will prefer B instead.
The influencers also do not have to be following an optimal strategy.
Honestly my AC batch is decent. Far better than the quality of reviews I’ve gotten for my own submissions.
I would prefer more reviewers to have a stronger opinion though, but usually each paper got 1-2 of those.
Since I'm moving over, I would like to reshare my most recent blog post on why you should learn Optimal Transport. Part 2 is coming, most likely during the winter break :)
mufan-li.github.io/OT1/
