My name's Bastian and I'm a researcher. I used to be a mathematician (like you?), until I found my love for computers and computer science.
These days, I am primarily interested in understanding how geometrical--topological information can improve machine learning.
Other than that, I am interested in building better academic systems, and help people navigate this jungle. I also enjoy general discussions on software development, literature, and much more...
We are very excited to share a new dataset chock full of interesting triangulations with you. In machine learning, a lot of works try to handle such higher-order inputs, but we show that there is still a long way to go. Let us know what you think!
Great selection of works, but I am missing a lot of references from topology in ML, with the article only assuming a very cursory perspective in terms of 'topology captures connectivity and/or continuity.'
Some works from my colleagues and me go a little bit deeper (no pun intended), for instance:
Maybe 'assumption' would have been the better choice. Given that HN is (for me!) broadly about playfully engaging with technology and society at large, I would expect that this _should_ attract a relatively diverse audience.
Fully agree with you there! As I said, I'm merely interested in getting a rough picture of the demographics (INB4: selection bias etc.). I find the statement you mention just as problematic as if you were to switch out the ethnic identity with _any_ form (self-identified) identity. That being said, I don't see anything problematic about looking at population demographics and then asking _why_ some identities are over- or under-represented. This poll should not be used for that, though :-)
I agree that this will be biased and I don't think this should be used for any serious type of survey. I was literally just interested in the results. :-)
They missed a great opportunity to call this BloTorch (Bayesian Learning and Optimisation?) here, but I'm very excited to see such methods gaining more traction!
Amazing to see 'magnitude' on the front page of Hacker News! If you are interested in a direct application of this invariant beyond ecology, check out our recent pre-print in which we study the generalisation behaviour of neural networks: https://arxiv.org/abs/2305.05611
My personal approach to magnitude is not based on category theory but rather based on weightings of a metric space. If your metric satisfies certain properties, you can obtain a measure of the 'effective number of points' of a metric space. This is particularly relevant when looking at the metric space from different scales---zooming in gives you a lot of disconnected points, while zooming out gives you clusters. Magnitude then captures the changes in the number of points in a principled manner.
I mostly write about academia and machine learning these days, but every once in a while, I also have the urge to write a really nerdy post on a more technical topic. Writing continues to be cathartic for me, and I hope to make a small difference when I discuss things that are not typically discussed openly (in an academic setting).
I use this on several of my sites as well. None of them get any significant amount of traffic, so I figured it would be okay to add more names to the list, as a small token of keeping their memory alive.
At least their servers are reasonably fast. I failed to mention in the article that this is also ridiculous: having to wait 30s to access your article, while clicking through three paywalls...
While I like seeing the Dunning--Kruger effect pointed out here as much as the next man, I have to comment that the original study was not as simple as it is often described.
> So the bias is definitively not that incompetent people think they’re better than competent people. Rather, it’s that incompetent people think they’re much better than they actually are. But they typically still don’t think they’re quite as good as people who, you know, actually are good. (It’s important to note that Dunning and Kruger never claimed to show that the unskilled think they’re better than the skilled; that’s just the way the finding is often interpreted by others.)
My name's Bastian and I'm a researcher. I used to be a mathematician (like you?), until I found my love for computers and computer science.
These days, I am primarily interested in understanding how geometrical--topological information can improve machine learning.
Other than that, I am interested in building better academic systems, and help people navigate this jungle. I also enjoy general discussions on software development, literature, and much more...
Here are some additional contact points:
- https://bastian.rieck.me
- https://twitter.com/Pseudomanifold
- https://github.com/Pseudomanifold
- https://mathstodon.xyz/@Pseudomanifold
- https://bsky.app/profile/pseudomanifold.topology.rocks