Banned for life is a stretch but the actual response is completely fine. They can just resubmit to the next conference.
Words mean something, if you promise to uphold a contract and break it, there are consequences. The reviewers were free to select the policy which allows LLM use.
Loads of researchers have only used LaTeX via Overleaf and even more primarily edit LaTeX using Overleaf, for better or worse. It really simplifies collaborative editing and the version history is good enough (not git level, but most people weren't using full git functionality). I just find that there are not that many features I need when paper writing - the main bottlenecks are coming up with the content and collaborating, with Overleaf simplifying the latter. It also removes a class of bugs where different collaborators had slightly different TeX setups.
I think I would only switch from Overleaf if I was writing a textbook or something similarly involved.
I see. You seem quite sure that Iran is not doing this - do you have some local source of information? My friends there said the government does shut down the internet at times (but I am not currently in communication with them...)
You think many are built without any assistance for coding? My impression was that people were mostly concerned about game assets like graphics and music
It is nicer to state theorems that hold for all vector spaces, so mathematicians like to invoke AoC. However, in any applications that are practically relevant, you can obtain a basis without invoking AoC.
I don’t think many researchers take peer review alone as a strong signal, unless it is a venue known for having serious reviewing (e.g. in CS theory, STOC and FOCS have a very high bar). But it acts as a basic filter that gets rid of obvious nonsense, which on its own is valuable. No doubt there are huge issues, but I know my papers would be worse off without reviewer feedback
That is a fair assessment. By and large it is used for the former. It is super handy in the exploratory phase of certain kinds of mathematical research.
Sometimes statistical rates for empirical risk minimization can be related to the intrinsic dimension of the data manifold (and noise level if present). In such cases, you are running the same algorithm but getting a performance guarantee that depends on the structure of the data, stronger when it is low dimensional.
Is this falsifiable? I would be hesitant to claim that this is unique to humans. I'd probably agree with dogs, but the line is much blurrier with primates, for example.
Typst seems like an improvement in many respects, but I definitely prefer LaTeX when working with detailed math equations (most of my use of LaTeX). I think there is a lot of inertia for anything to replace LaTeX for mathematical research.
Is this true on Mac? Usually I am notified when programs request access outside the normal sandboxed or temp folders. Not sure how that works in any detail though.
I think formal systems like Lean are still extremely interesting. And I would imagine that, if these machines get good at standard "informal" proofs, that the overhead of formalization would be a lot less painful for them than humans (it is pretty tedious these days but even for humans the friction is decreasing).
Verified proofs allow you to collaborate with much less trust (for humans or machines), at least in the phase where you are trying to figure out what is true. They don't guarantee that a proof is insightful for "why" a result is true, but that is much easier to put together once you have a valid proof.
I'm all for high quality captions, and turn them on occasionally when dialog is hard to hear (or in another language, usually prefer to dubbing). I also don't doubt that they improve comprehension. But on average I find them distracting from the visuals and prefer to have them off by default.
Yeah in context I somewhat agree, though the utility for graphics applications probably comes down to some more empirical aspects that I won't conjecture about. I imagine there is some stuff you could do in this setting by incorporating autograd derivatives from many slightly perturbed points in a neighborhood of the input point (which together act as a coarse approximation of the subdifferential set).
If a function is Lipschitz, then it is differentiable almost everywhere by Radamacher's theorem. Moreover, if you want to prove things about Lipschitz functions, you can often (though not always) prove things about continuously differentiable functions with bounded gradients, and then lift to all Lipschitz functions via an appropriate compactness argument.
Any reasonable statistical explanation of deep learning requires there to be some sort of low dimensional latent structure in the data. Otherwise, we would not have enough training data to learn good models, given how high the ambient dimensions are for most problems.
So much funding is being paused and cut though, who's to say if your field of study will still have federal funding in 1,2,4 years? This lack of funding stability is making US academia way less appealing.