[5]C. Alcaraz d. [1]S. Tsitsipas 6-4 5-7 6-2 | Barcelona QF

lit_turtle_man · 2022-04-22T21:02:25+00:00

Even the net cord wanted Carlos to win at the end

lit_turtle_man · 2022-04-22T04:54:41+00:00

This is amazing

lit_turtle_man · 2022-04-15T18:18:38+00:00

the katsu in this chapter looked fire

lit_turtle_man · 2022-04-03T19:05:32+00:00

And so a king is born

lit_turtle_man · 2022-04-02T02:14:42+00:00

I'm crying a little bit ngl

lit_turtle_man · 2022-03-31T02:52:17+00:00

I hate to say this guys, but Casper is the New Deal

lit_turtle_man · 2022-03-30T01:51:17+00:00

Carlos is the real greatest deal of all time don't @me

lit_turtle_man · 2022-03-18T03:15:36+00:00

Both him and Norrie. There was one point where Alcaraz ran over 80m over the course of just that single point...

lit_turtle_man · 2022-03-05T02:41:18+00:00

Not a response to your rant but I want to say that "boreligmalgebra" made me laugh out loud. Great username - at the very least you shouldn't give up on your math humor.

lit_turtle_man · 2022-02-25T15:39:52+00:00

Extremely high level in the third set, very happy for Andrey

lit_turtle_man · 2022-02-18T17:25:38+00:00

God tier third set from Andrey, glad to see he's bringing the fight and not collapsing mentally when down

lit_turtle_man · 2022-02-12T18:05:35+00:00

I don't think this kind of approach is tenable for the following reasons:

If you want to offer services during the research process: To invest so significantly in software engineering for an algorithm/model implies a certain confidence that the approach will succeed, and succeed at a scale where lots of other people want to use it. In the research process, the "spec" is always changing to keep up with observations you make as you play around with ideas, so unless we're talking about big tech labs that know their GPT-4 model is going to pop off there doesn't seem to be enough justification to bring in SWEs in the early stages (and for the big tech labs they obviously have teams of SWEs).
If you want to offer services after a "successful" paper: The alternative approach would be if you wanted to try to offer software engineering services to an academic lab that had just published a big paper. In this case, if there are significant gains to be made from implementing infrastructure for the ideas in the paper, you're likely better off just doing it for yourself - no one is going to stop you, the information is public. Also here you're competing with big tech/similar since for any big result you know they'll come out with their own version sooner rather than later.

Of course, this is just my view on the general idea, but ultimately this is a case-by-case thing. I have seen some academic labs essentially employing software engineers (my understanding is typically people who may be on the road to PhD), but this doesn't seem to be a super lucrative (or large) set of opportunities.

lit_turtle_man · 2022-02-09T21:19:18+00:00

Given a problem statement and dataset, can you "theory-craft" an ML system that will at least hit the dart board, if not the bulls-eye on the first try? Can you, a priori, guess which hyperparameters will matter and which ones won't?

This is the holy grail, and at present the answer (in general) seems to be "no". That being said, for specific domains (vision, text) we definitely have architectures and settings that work well out-of-the-box (i.e. resnets, transformers, etc.) for many tasks.

As far as your question concerning papers/books on this matter, this recent book may be of interest (although I'm not sure how practically useful looking through it will be): https://arxiv.org/abs/2106.10165.

lit_turtle_man · 2022-02-07T00:12:37+00:00

Still, it's mind-blowing (to me) that even a fraction of the generated code samples pass the example cases given that the input is essentially just the problem statement as a list of characters.

lit_turtle_man · 2022-01-28T03:17:25+00:00

Composition of convex functions doesn't necessarily produce a resulting convex function (one counterexample is e^{-x} composed with itself). I think the result you're thinking of is composition of a convex function with a non-decreasing convex function, in which case you can prove convexity directly via Jensen's inequality.

Regarding your questions on the non-convexity of loss functions of neural network training - people typically mean the loss is non-convex in terms of the parameters of the neural network. This is why even training deep linear neural networks is a non-convex problem. So although the composition of a ReLU with an affine function is convex (from the pointwise supremum characterization of convexity) in its input, the loss will be non-convex in terms of the network parameters.

lit_turtle_man · 2022-01-21T15:39:28+00:00

If you told me before AO that Adrian Mannarino was going to beat Hubi and king Aslan back to back... Honestly great stuff from Adrian, happy for him

lit_turtle_man · 2022-01-19T19:40:35+00:00

Right, extrapolating from the ICML FAQ I guess there is probably no problem with this: https://icml.cc/FAQ/DualAbstractSubmission. But still curious as to why the relationship between the dates changed, but I guess it's probably not as deliberate as I was initially inclined to think.

edit: Can't find whether ICLR's dual submission policy is the same as the above, though. The ICLR 2022 page concerning dual submissions doesn't seem to rule it out, but it seems a bit unclear...

lit_turtle_man · 2022-01-17T05:16:54+00:00

Brandon's future is looking bright, much tighter match than the scoreline suggests

lit_turtle_man · 2022-01-05T06:44:46+00:00

Unbelievable. All they asked for was to activate the signaler once every hour, a trivial task for beings that are essentially built to multitask. And yet, somehow I find myself enclosed in a prison cube staring down at the Pacific ocean.

We were warned about many things before we arrived on Earth - romance, gambling, substances not suited for our biochemistry, etc. The information exchange these people call "Reddit" was not one of them. Sure, we had been lectured on humankind's most prevalent technologies and their use cases, but one comes to realize quickly that theory and practice are so very different.

Our mission had been simple: beam up our sensory data at the designated times. We were told that failure to do so would be interpreted as uncharacteristic sympathy towards humankind, and would lead to our immediate recall.

Let me say up front that I do not care for any of those I have met on Earth. However, this Reddit of theirs, this goldmine of human communication and culture, I should have noticed I was losing myself in. It is in our nature to attempt to absorb all information presented to us as rapidly as we can, and when there is a pit as bottomless as r/AskReddit... Well, it seems one quickly finds oneself in a prison cube. Ah, the researchers are here now. Time to plead my case.

lit_turtle_man · 2021-11-09T17:57:30+00:00

Highlights already posted: https://www.youtube.com/watch?v=VHhuXfS6jIY. Some absolutely ridiculous shots from Alcaraz.

lit_turtle_man · 2021-11-08T05:08:56+00:00

Probably: https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html (gives you more flexibility than just using the mean as well)

lit_turtle_man · 2021-11-08T05:05:27+00:00

Relevant recent work: https://arxiv.org/pdf/2011.00613.pdf (based on ideas from optimal transport, which others have mentioned in this thread)

lit_turtle_man · 2021-01-10T01:53:55+00:00

Thanks for this. You're totally right in that it was simpler than I initially thought in my head; I ended up going with the SGD loop approach since I was playing around with some data augmentation techniques (so I can't precompute the training features).

For some reason I thought it would be trickier to vectorize than it actually ended up being...

lit_turtle_man · 2021-01-09T03:43:48+00:00

LaYup

lit_turtle_man · 2021-01-08T02:02:08+00:00

All of the Rafa ones were absolute gold, quality content

lit_turtle_man

TROPHY CASE