Hmm, interesting! Do you have any papers/article/sources supporting this claim?

Have tried CycleGAN, CUT (which is an improvement of CycleGAN), NEGCUT (similar to CUT) and ACL-GAN.

Could you recommend any SOTA models using U-NET?

Ah right, then you can just use the model's reward directly or pass it through a sigmoid so that the reward is between 0-1!

Do you think that the sigmoid is needed?

Architecturally probably some form of unet is best. It’s the architecture of choice for things like segmentation so I imagine it would be good for IR as well

Have you already tried different variants of GAN for more stable training?

Autoencoders like VAE’s should work better than any other models for image to image translation. Maybe you can try different VAE models and compare their performance

I was wrong.

it's just a binary pairwise comparison of which is more preferred between 2 outputs, read the instructgpt paper or the wandb post https://wandb.ai/carperai/summarize_RLHF/reports/Implementing-RLHF-Learning-to-Summarize-with-trlX--VmlldzozMzAwODM2#train-the-reward-model

First, I’m blown away that you are suggesting that you don’t know your students and their writing styles, some of which are performed in-class and almost all of which differ significantly from the way ChatGPT writes, but second, my teachers said the exact same thing you are saying decades ago and freaked out when CliffsNotes came out!

Re-read my prior argument because nothing you just said impacts it, and it still stands.

yeah, instructGPT is like that. How do you calculate a reward score for each output in this ranking scenario?

chatgpt (assuming they use same training as instructgpt) doesn't use a numerical scale, everything is a comparison between 2 (out of k) sampled outputs from a prompt, so everything is a pairwise comparison

You don't really need a separate extension, do you? Your bot can just be another user submitting the timestamps.

Though it would help if the extension developer provided a list of videos that are being watched by their users but has no timestamps yet, so your bot isn't spending time scraping though unpopular videos.

I have an autoencoder input of 100x21. The 21 columns are PC scores, the 100 rows are observations. The importance of the columns degrades as the column number increases. The first column is the most important for the data variance, the last column is the least important. To be able to reconstruct the data back from PCA the first columns need to be as correct as possible.

I have tried searching whether I can adjust weights or something else of the autoencoder layers to include this importance of the columns, but I have not found it.

In other words, I want errors in the first (e.g 5) columns to be punished more harshly than errors in the last (e.g 5) columns.

I would be grateful if someone could point me in the right direction!

...or...you could just get the youtube adblock/sponsorblock skip extension (dunno exactly what it's called SkipAdTrigger or something? I cannot check my home machine at the moment...but it's available for Firefox and I'm pretty sure something similar must exist for other browsers as well).

Works well in my experience. It automatically skips sponsorblocks and marks them as green on the time bar (so you can manually watch them if you're into that kinda thing. Hey, there's all kinds of kinks out there. Don't judge.)

Thanks for sharing. What is the Pile? Never heard of it.

2 seems doable. Not everybody has to have a GPU, but I bet lots of people, including me, would rather spin up the GPU in their personal computer for a few seconds than manually specify where skippable segments are.

The one central server thing bugs me. I'd prefer something like "query your nearest neighbors and choose the one with the most recent data." No idea how to do that though; not a systems person.

You should check out cs229m stanford

OK I can almost see this working, thanks for the suggestion. The only thing that would prevent me from implementing this solution is that by taking the sum of the two models, it would let m_1 give as equal a contribution to the result as m_1. However I expect a single days data to be noisy, Thus I would need the contribution of the new days data to be down weighted somehow.

ChatGPT is beyond cheating. We have to go on the default that it is applied in essay writing. And surely you agree that it is pointless to assess the output of a machine. Therefore, essay writing will cease to be a method of assessment, and consequently, whichever way you look at it, future students will no longer learn to write.

The following work shows that with sufficiently large width (overparameterized regime) you can have polynomial convergence to the global minimum which gets worse (but polynomially) with the depth of the network.

A Convergence Theory for Deep Learning via Over-Parameterization

Prior to this, have you experimenting with smaller (== more manageable) variants of this model or previous variants were attempted directly at this scale?

Honestly, I'm not convinced it needs a hugely complex language model, as (to me) it seems like a primarily classification task, and not one that would need a deep level of understanding. It'd be a level or two above standard spam filters, maybe?

The two primary NN-in-web solutions I'm aware of are tf.js and ONNX Runtime Web, both of which do CPU inference, but the latter is developing some GPU inference. As you say, it only needs to be done once, so having a button that scans through the transcript and classifies sentence probabilities as sponsor-read or not, and then automatically selects the boundaries of the probabilities seems readily doable. Even if it takes some noticeable amount of time for the user, it's pretty quickly amortised across the entire viewing population.

The only real concern I'd have at that point is... is it worth it for the average user over just hitting the right arrow two times and/or manually submitting the timestamps themselves? I suspect that's why it hasn't been done yet

Amazing work, I've been following this for a while. Have you considered putting this into an arxiv whitepaper describing the model + tricks? I've wanted to cite this a couple times, but have had to resort to citing the github repo.

Godspeed to you. I think the first person to get it to the chrome/firefox extension store would get the most downloads and pave the future for all other adblocking/sponsorblocking extensions (coz no other extension currently does that, AFAIK).

I don't have much experience of the cost of training NLP models (I work mostly in Vision). But I think if you can get a product out with just enough accuracy to get the heads turning in your favour, you could always scale up the model later down the road. Alternatively, you could have donate button on the extension's settings page (which many extensions do), if you do get some donations you could use it to update the model later on. It could be crowd-sourced and crowd-funded simultaneously.