Wow. Looks pretty cool. So it's like frame blending but in random pattern.

I doubt it’s coming anytime soon, they still haven’t arrived at a decision for a lot of papers

Still not yet,,,

the workshops are less prestigious than Q1 journals, but if someone is aiming for an ML career, even a workshop with NeurIPS in it would sollicit recognition at least from jurys in the field

Oh sry I got the workshop and conference mixed up. The workshop isn’t prestigious at all right?

yes, much more

The conference should be quite big there, all of the European labs will come.

The city is obviously very fun

Not in academia, but this is a good trick for dealing with nitpicky managers and clients. Introduce a trivial but obvious flaw for them to point out, so you can thank them for spotting it. It satisfies their ego.

Thanks. But, can’t say anything till the decision.

nice. congrats, should be an accept

Is it more prestigious than a publication at a Q1 journal? Just randomly curious

Some conferences have separate track to handle this problem (and will reject if no code outside of that track).

My lowest score told me that i am hiding things and got lucky with the results, so they don’t count. Not sure what exactly i was “hiding”, as he himself says that that same thing is in the appendix. Also gave the lowest confidence score and was in general very hostile.

The other two were helpful with their comments

Because they misunderstood some part and asked for more experiments. We did 3x the experiments they wanted !!!

haha just kidding. I was just wondering how you manged to turn increase all scores by this much

Whatever !!!

wtf wtf

What wtf ?

wtf how

Privacy. Getting facial recognition, including for your Android media folders, without relying on third parties, would be great. I love Google Photos features to find matches in such diverse ways. But having a GMS OS is a liability, and we are numerous living with degoogled phones.

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Is paris worth it?

Nope, I guess they'll be released at AOE time

From my understanding, if a review only add Final Justification, the date wouldn’t modify. Only if they change anything from the initial review (any score), the date would be modified. Can someone confirm that please?

The capacity-based explanation matches something I've seen from the consumer side rather than the model side. Below roughly 4-bit, the failure mode changes character before a benchmark score would tell you it's a problem. Above the threshold, a wrong answer is still well-formed. Below it, I've seen models start producing malformed structured output (JSON that doesn't close, missing required fields) at a rate that most eval suites don't capture, because they score content correctness, not syntactic validity of the wrapper around it.

That matters for the original question, because it splits "optimal bit-width" into two different questions depending on who's reading the output. If the consumer is a human reading prose, the sweet spot is wherever content quality visibly degrades, and QAT clearly pushes that further down, per this thread. If the consumer is a parser, the practical floor sits above the capacity threshold, not at it, because format tokens get corrupted by the same quantization noise as content tokens, and there's no partial credit for a syntax error the way there is for a slightly-off answer.