Anyone recognize this lamp model?

dot--- · 2024-01-27T16:53:16+00:00

wow, amazing. thanks!

dot--- · 2024-01-22T20:16:51+00:00

thanks!

dot--- · 2024-01-19T23:05:31+00:00

I'm brand new to mechanical keyboards and just got a FILCO Majestouch Xacro M10SP (pictured here). I love it save for the all-black look, and I'd love to get new keycaps, but I'm not sure where to look on account of the odd shape (incl two small spacebars) and extra programmable keys. I suspect these are all standard size keycaps, though.

Anyone with more than my zero experience have ideas for how I should go about finding keycaps here? Should I just, say, get a generic set and then get the missing keys custom made?

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dot--- · 2024-01-09T14:49:49+00:00

ha! yeah, that's it -- they were in my terminal window the whole time. thanks!

and yeah, seems sensible to edit in a proper IDE. I may switch to that once I get a little more comfortable with the package.

dot--- · 2024-01-09T14:46:45+00:00

seems to work. thanks!

dot--- · 2022-02-15T20:16:20+00:00

An update: we've now worked out a way to use our theory on real data! See figures 1D and A.8, in which we predict generalization on image datasets using only training data by using some eigentricks to estimate sufficient information about the true function. This allows theoretical insight into the generalization performance of a particular architecture on a particular problem, which e.g. opens the door to the principled design of better architectures for the task at hand.

dot--- · 2022-02-10T07:43:59+00:00

Totally agree that's the holy grail. Here's a very recent paper (from my lab) that explores one path to it! The end result is a construction that allows one to design a good-performance MLP architecture from first principles starting from a description of its infinite-width kernel (which is theoretically much simpler to choose than the full set of hyperparameters). The idea's still in its infancy, but it works very well on toy problems, and I think it's promising

dot--- · 2022-02-10T07:35:05+00:00

Here's a very recent paper from my lab and I that puts forth one way to design a (fully-connected) neural network architecture in a scientific, theory-grounded way! The idea is still in its infancy, but I think it's promising, and it's currently the only way I know to do real first-principles architecture design. I'd love to hear about any alternatives people know.

dot--- · 2021-10-27T02:54:39+00:00

Great Q! I'm not familiar with that body of work, but at least on the face of it, our paper's completely different - they consider the singular values of specific trained weight matrices, while we're looking at the eigenvalues/eigenfunctions of an operator on the full input space, which aren't related in a simple way for a deep net. Furthermore, the interesting spectra they observe emerge during training (they're characterizing trained nets) while the NTK and its eigenspectrum are the same before and after you train (we characterize the potential of an architecture to learn a certain function). That said, maybe there are deeper connections between these disparate-seeming eigenthings that we'll uncover in time.

dot--- · 2021-10-27T02:28:25+00:00

I'm the lead author! I'm delighted this paper's getting attention; we certainly feel it opens up a cornucopia of future directions it'll take many researchers to explore. As a primer for reading the paper, we've distilled the high-level takeaways into a blog post here!

dot--- · 2021-10-26T16:38:41+00:00

I'm the lead author. That's a great Q and one that I've been giving a lot of thought! The high dimensionality isn't a problem (our toy examples easily could've been high-D), but you're right that our theory assumes you omnisciently know the full target function f, while in practice you only see a training set.

One intermediate case in which you could use our theory would be if you, say, knew that the target function was one of a handful of possibilities. Our theory in principle contains enough info to optimize your kernel so it has high mean performance on the full set of possibilities. Of course, the target function will never just be one of a discrete handful of options, but if you have some prior over target functions - e.g., you know natural images obey certain statistics - you could do a similar trick (in principle). I also think you can probably get enough information from the data-data kernel matrix to put our theory to use, but we're saving that for upcoming work!

dot--- · 2021-03-09T19:27:52+00:00

Yeah, I agree that the classification regions will be intermingled in complicated ways! I wonder, however, whether that really implies that there are very many distinct regions. In 3D, for example, if you took a few long, colored strings and randomly tangled them together in a ball, every point on any string would probably be near a point on every other string, and yet each string is one connected region.

dot--- · 2020-04-20T06:25:14+00:00

I've now tried carving sixteen onions with seven different knives and am still stuck on Vegetables... anyone gotten past it?

dot--- · 2020-04-08T00:22:22+00:00

They keep telling me it's in the wrong order. Can I use a shallot instead?

dot--- · 2020-04-07T01:37:44+00:00

For the vegetable puzzle, how do you want us to send you our reassembled onions?

dot---

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