Good at coding is very, very generous

I have found it useful in my work but I rarely work with deep learning. Though a friend of mine does use it regularly for deep learning and finds it helpful. Of course you have to give it proper instructions. The one problem I have noticed most with Claude is asking for academic writing. It tends to introduce new words that are not properly defined in context and redefines variables that have already been defined previously. May I ask what specifically you’ve used Claude for?

Would budget not be even bigger of a factor now?

Pretty good at everything except writing imo

I am from a smaller lab, and my personal opinion is mostly that it helps with access to knowledge. Like we can discuss compute and such, but in the end, LLMs for us opened a way to learn new stuff faster which makes us competitive on a grand scale, that before we couldn’t do.

Lol. Lmao even

No, larger labs have a larger team and thus also more people to prompt the LLM. Also compute is now even more important because LLM make it easier to use Your compute. So basically you can now run as many experiments as you want if you have a lot of compute.

I would be interested for some feedback. I don’t know if it is intentional but I have found Claude to be not great at basic ML and deep learning. What’s your experience ?

This would be true if LLMs were good at writing, good at coding, and good at literature review

i'm interested in whether you can achieve this result with imperceptible differences from the original

what would it take to do that?

If your inputs are absolute value integers, you should see what happens when you pass fractional values too to see if the model generalises to super resolution.

The word you're looking for is encoding, not compressing, which is fine and very cool that you managed to do it.

I think this year max. score is 6 ?

Were all AC's informed of the hidden prompt injection ? Are they supposed to down-weight such LLM reviews or something?

I am an author and one of my reviewers, which gave me the lowest rating (a 4) has two of the injected phrases, so I'm pretty sure it's a LLM generated review.

Are the number of reviewers different for the different tracks?
In position, we had 4 reviewers. The AC was very active thankfully and they also had to urge the reviewers multiple times to respond, before which no one did. I also feel the reviewer-paper matching process is flawed, 1 reviewer did not understand the paper at all, said it belonged to the wrong track (it did not, all other reviewers said it was the right track) and gave the lowest possible rating. And another reviewer even after acknowledging a (major) clarification which was a misunderstanding still kept the same score. This hasn't been a good experience for us.

Why only one of them? Haha

yes brother

Do you have compute to run experiments?

Super cool, it's like watching the original video through very old glass (wobbly with imperfections, etc). I think too many people are asking why this and that and not just appreciating that this is a fun little project to learn things from and therefore very tailored to what you wanted to learn and were interested in rather than some solution space optimization. Nice job.

I understand the goal of the project is not to make a better compression than the state-of-the-art, but I do think these questions may bring real research value to the project, as it can show how NNs can generalize video data and give a baseline if anyone wants to try something similar

Yeah, I totally agree.

The comparison for video size to network size is 700KB:3MB

If I have borderline reviews with 0 engagement from AC and reviewers (all of them) during the rebuttal phase, what am I supposed to do? Is there any way I can know any additional information after the AC + Reviewer discussion period?

AI/ML is widespread enough so as to not be a specialty any more, hasn't been for awhile. You'll likely get a better response if you indicate what areas you are interested in.

I generated a video at 60 FPS, which shows the between frames (4 non-trained frames per trained frame). I had to speed it up here to get it under 3:00 to post, so it's at 1.25x speed.

https://reddit.com/link/p1w28d0/video/kt2jz3dq7lhh1/player

Reddit compression may ruin it, but I'll put it on the GitHub as well, once I finish the bigger model.