If you have Y = A B X, then is M = A B full rank? If not, then they're not even equivalent.

Technically, and I emphasize the technically, the set of function represented by a neural network require only one layer. However, there is little guarantee that you can feasibly find the proper configuration or train the network accurately.

By adding another layer, you can reduce the training burden by spreading it across layers. The extra dropout also allows more regularization.

This is the part of deep learning where it's less science and more, "eh, sounds like it works."

If you write a good rebuttal I'd be hopeful. You have two reviewers undecided, and one apparently willing to champion your paper. In my experience, if things do shuffle after the rebuttal, ratings tend to gravitate towards the pre-rebuttal mean. In your case that would be a good thing :)

2 accept and 1 boarderline

People who got orals in the previous years, what was your average score

Can you change the title/abstract of CVPR 23 submissions during/after the rebuttal phase? Some reviewers have trouble with our title and think we should change it. Can we commit to doing that in our rebuttal response?

I have a small images dataset labeled on cvat. Now I need to export it and train the network on pytorch lightning. How can I do that? I'm a complete noob on this but I need it for the next phase of a project I'm working on.

Any help is realy apreciated!

I think it does mess them, alibi paper seems like better solution.

2 borderline (3), 1 weak reject (2) and a reject (1). Both borderline seem possible to change. The other two might be able to increase by a point. What are the odds? Or should we withdraw? Thanks :)

2 borderlines (3), 1 weak reject (2). The confidence level of both borderlines is 4, and for the weak reject its 2. Do you think there's a chance for flipping and acceptance?

Possible with a good rebuttal and a little bit of luck that the reviewers are willing to change their mind. But borderline suggest that their opinion is not set in stone, so not a bad sign, and the accept will point them in that direction.

1 weak reject, 2 rejects 😢 This is also my first ever submission to any conference ever. Time to learn to not suck lol

Phd student here.

1 accept (5), 2 Borderline (3). What are the chances of my paper getting accepted?

Sure! So this is for audio signal processing. There is an amplifier that takes an audio signal and volume as input. However higher volume causes white noise, so I want the volume to stay low whenever possible and boost the volume by multiplying the input signal instead. But of course the multiplication won’t work if the input to the amplifier itself is already high. Switching the amplifier volume too much is not good either as that would cause pop/click noise. So I’m designing a small neural network that will take the audio signal as input and output the amplifier volume. The way I went about is I modeled the amplifier and all noise associated with it using tensor math. Then I used the amplifier output minus the original input and did MSE on that. Note that the audio signals are pretty long so the filter+MSE is a pretty massive expression. It seems to be working somewhat, but not sure if there is an easier way to do this…

3 is borderline 4 is weak accept, yes

What would you recommend for a good resource for learning AI/ML. I have some knowledge in web dev and know C/C++. I finished the OdinProject foundations and currently on Full stack JavaScript but I kinda got a bit curious about Machine learning and I would like to get my feet wet. Is there any good resource to start, what would you recommend?
Don't really care for udemy courses and watching a lot of videos cuzz I've tried it for web dev and it just feels like tutorial hell, but I loved The Odin Project and reading tutorials/documentations/doing exercises/projects because I actually learn a lot that way. I've seen websites like mlcourse.ai and kaggle.com but still haven't tried them. What is your opinion on them, maybe a comparison to theodinproject.com and would you recommend something else

May I please ask whether borderline corresponds to a score '3' and weak accept to a score '4'? thank you

http://people.csail.mit.edu/khosla/projects/cvpr_review/

A study from cvpr 2012 looked at the distribution of reviewer scores + accept/reject decision. It's definitely outdated given how much cvprs grown but its a fun way to procrastinate working on the rebuttal

Would you mind giving more details about the domain and the purpose of the loss function? Maybe people can give you hints based on that.

Is there any public statistics of past years CVPR? Like what is the average score for those who got published??

2 Weak accept and 1 weak reject.
Is there any chance for me?

Common wisdom is to not bet on flipping, but it is a case by case thing and it can happen.
Read the reviews and if you can deliver a good, carefully worded rebuttal you may flip one or two. But in the end it depends on how open the reviewers are to flip.

Agreed -- in some cases. Depends on the level of the student, if they are studying in a class etc. My goal was to write the first thing you should read about each area.

1 borderline, 2 weak rejects. For the first submission, it’s okay. Is there any statistics on changing the recommendations after rebuttal?

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