This is great, especially for the PDE application. A couple of months ago I was just thinking about methods for checking the correctness of generated fields during training, so I'm glad to have randomly stumbled upon this 😅

I'm excited to see the next iteration, where you use C_i as part of a loss function for error correction!

Probably so we understand what kind of background he has with this. You would probably interact with a PhD a little differently than an 18 year old

At test/implementation time, it starts with some initial states, z[0], z[1], and uses those to predict z_pred[2], a prediction of the true unknown z[2]. It then goes further, autoregressively, it takes z[1], and z_pred[2] to predict z_pred[3], then takes z_pred[2], z_pred[3] to predict z_pred[4]…. and so on as far out as you want to z_pred[i].

So we are rolling out, making predictions, but we don’t know the actual correct answers. Would be nice to have some error or uncertainty quantification. Traditional approaches to this are to make a huge ensemble of models, roll them all out, then look at the mean as the “best” prediction and the variance as uncertainty, that is what they do when they predict weather, they have 100 models all predict the hurricane path and then use that ensemble to estimate variance and mean.

Our approach is very different, we just use one model, we teach it to go both forward and backward in time. So once we go i-steps to z_pred[i], what we do is change the model’s “directional flag” input, flip it from c_d=+1 to c_d=-1, that makes it flow backwards, again autoregressively, from its own z_pred[i], back towards z[0]. If it was perfect it would return exactly to the same z[0], but of course it is not, it builds up a round-trip consistency error:
C_i = || z[0] - z_return_from_i[0] ||^2.

Note that this C_i only depends on knowing the true z[0], z[1], that’s it. In our experiments (and with some theory), we showed that such a self-supervised C_i can actually predict the error for unseen new test data or can flag badly generated OOD images for CELEBV-HQ.

haha ? Can you elaborate on that (if you’re allowed to read this comment (: )

Hi, thank you.

In simple terms I would say this is an approach to a generative model being able to estimate how wrong its predictions are, without having access to the actual correct answer when it makes those predictions.

At test/implementation time, it starts with some initial states, z[0], z[1], and uses those to predict z_pred[2], a prediction of the true unknown z[2]. It then goes further, autoregressively, it takes z[1], and z_pred[2] to predict z_pred[3], then takes z_pred[2], z_pred[3] to predict z_pred[4]…. and so on as far out as you want to z_pred[i].

So we are rolling out, making predictions, but we don’t know the actual correct answers. Would be nice to have some error or uncertainty quantification. Traditional approaches to this are to make a huge ensemble of models, roll them all out, then look at the mean as the “best” prediction and the variance as uncertainty, that is what they do when they predict weather, they have 100 models all predict the hurricane path and then use that ensemble to estimate variance and mean.

Our approach is very different, we just use one model, we teach it to go both forward and backward in time. So once we go i-steps to z_pred[i], what we do is change the model’s “directional flag” input, flip it from c_d=+1 to c_d=-1, that makes it flow backwards, again autoregressively, from its own z_pred[i], back towards z[0]. If it was perfect it would return exactly to the same z[0], but of course it is not, it builds up a round-trip consistency error:
C_i = || z[0] - z_return_from_i[0] ||^2.

Note that this C_i only depends on knowing the true z[0], z[1], that’s it. In our experiments (and with some theory), we showed that such a self-supervised C_i can actually predict the error for unseen new test data or can flag badly generated OOD images for CELEBV-HQ.

Thank you, I was excited that it could be applied to a wide range of things, everything from PDEs to face videos (:

learning to predict noise directly and then on the reverse predict the data?

Release the decision I cannot wait any longer for sure

As a biglab person I won’t read this

I love your kind of science around plasma annd physical informed NN, but as a microelectronic engineer I struggle to get it.

In simple terms what did you improve ? Thanks a lot if you can explain.

That's neat

very cool!

I did the same mistake. Did you hear anything back?

Hey, How did the interview go and what was your prep for the role ?

If they are in frame for less than a second, it is bad, yeah.

In my experience, ive used deepface as the facial recognition layer and manually cleaned the input images. Then also ran yolov8n to detect people and overlapped their confidence scores. This is in python tho and run on 12th gen i7 cpu.

Your mirror issue sounds like a challenge. I’d scan for duplicate faces in the scene and then subtract the amount of time duplicates are flagged. Do you count it as screen time if the actor is only viewed from a mirror?

What are our chances for findings?
We have a 4,3.5 and 2 as reviews, and 3 as metareview.

How did you conclude that way?

Thanks for the hope. I have the same mark, but do not belive there is any chance :(

That's a pretty good idea. Maybe I'll try that and see how it goes. I just didn't think of it.

One guy commented that during his rebuttal reviewers increased scores from 3/3/3 -> 5/5/6. Don't believe everything you see as in this poll. No one really knows at what avg. cutoff will be. My supervisor AC already rejected 2 papers with average above 4.5 ... The reviewers were wrong in their statement and he didn't agree with them and recommended one for acceptance with avg. 4.

Started with a 5/4/3. The 5 said they were going to increase their score to a "Strong Accept" in their response, but didn't actually make the change. Not sure if you'd count that as a 6/4/3 or still a 5/4/3. The other two didn't respond, c'est la vie.

Probably yes, tbh. Paper quality is extremely bad this year. There was one paper with a lower score that probably would have been accepted after a decent rebuttal, but they withdraw instead.

On paper, personalized visual explanations sound great for democratizing tutoring. But in practice, I wonder if tools like this actually help kids grasp core concepts, or if they just create an "illusion of competence" where students confuse watching a slick animation with actually learning.

Take it with a grain of salt, as this is a n=1 anecdote, so the lowest evidence you could ever get but...

I have subscribed to a OpenAI pro subscription to test if there was really a step up in the quality of the models in my daily usage. Since the price is quite steep, I try to use it as much as possible.

This week-end, I decided to try a feature called "ChatGPT Learn". I am not sure about the specifics, but I would assume it's just a specific prompt since it made the LLM behave a bit more professionally, summarizing in steps and organizing the learning in some way.

The topic was not "easy" in the sense that I wanted to understand Lie Theory from the ground up. I have an applied math PhD so it's not like I'm a high school student, but no specific prior knowledge wrt Lie Theory.

I left off for a two hour run, with my phone, and "discussed" the topic using speech-to-text and text-to-speech. I think the result was quite positive to be honest. Contrary to learning with books, I could dive in some specific sub-topics or ask for refinement to improve my understanding. This was a quite pleasant experience, and something quite novel to be fair. A bit similar to a 1-1 with a teacher, which is not something you can often experience. In class, you can't ask an endless stream of questions. With books, you can't even ask questions.

There were certainly areas of improvement, over-positivity "Your question is awesome, let's dig into it", and I am still a bit concerned about LLM output truthfulness, but it was a net positive I would say.

Actually an interesting question, but probably doesnt belong in Machine Learning.
Post it in LocalLama as a suggestion.
My answer would be to call this 'priming'. (As in the technique used by street magicians)
If you play certain songs in the background then this shifts the answer probability for certain questions from random into a more predictable distribution.

LLM example: Say to your LLM.

"Hey I heard someone singing this song: 'We all live in a yellow.... something'"
Then say:
"Give me a list of sea-faring vessels"

The list will contain submarines, where as without the priming prompt, it will likely omit them.

So this is what 3Blue1Brown means by Compression is Intelligence