Open source doesn't mean freely used. That's the whole reason there's an F in FOSS.

Important though to note are literally not enough people just taking stuff implemented in scikitlearn or whatever and applying that to their own problems, and in and of itself that can be novel and interesting even if its not a shiny new model.

> As I said domain knowledge and/or providing data and relying on the technical expertise of others is the most valuable direction to go.

Its mainly the way you wrote your comment that left a bad taste in my mouth, this line specifically is probs a fine recommendation for OP.

Do you actually work in ML research lol? About as important as fundamental research on architectures losses and optimisers is the applied end of things and tons of applied stuff is absolutely something other domain experts can contribute to, non ML non CS expertise is absolutely essential to i.e. the stuff my group does. "State of the art on some famous benchmark" is not the be all and end all of this field and "only a small minority is able to make significant contributions" is an absurdly incorrect statement.

You want this done in a controlled, methodical and documented manner, not earlier research which showed SD 1.5 to verbatim copy every line and minute contour of wood grain in a specific copyrighted "wooden table" background, found after training to be repeated tens of thousands of times in the input dataset (due to websites selling phone cases photoshopping phones onto it).

Its also an issue for generator training though if the discriminator gets 100% all the time, if I remember correctly. Theres various stuff you can look up to make training more stable which I don't have on hand rn.

Idk what he means specifically by the "gradient being passed between" two agents but in a GAN (part of) the loss function of the generator is the inverse of (part of) the loss function of the discriminator, so the gradients calculated at generator output and discriminator output are linked.

A less formal way of saying it: The generator's gradient depends on the discriminator's loss.

This should be true for any adversarial game, I would think?

Much better to rent lol.

Got it, thanks! Yeah, I guess it's more complicated than I thought.

Hope you get the help you need 🙏.

Lol at your previous posts. https://www.reddit.com/r/LateStageCapitalism/comments/zdeix8/ai_art_is_very_dystopian/j044ec2/

You are obviously disturbed and just latching on to arguing for thousands of words online as an outlet.

Cannot wait for next month when Ukraine or COVID-19 is back on the news cycle and you move on to /r/worldnews.

Your model is learning to do is predict future market data from past market data, which fundamentally is not worthwhile because market data hinges on real-world news. If you want massive quantities of real-world news data in a structured/tagged format, look at GDELT.

https://www.gdeltproject.org/

Also, look at using Kaggle's GPU notebooks instead of Google's. You get 30 hours a week if you verify with your phone number, instead of Google's arbitrary secret heuristic based cutoff. Or look at something like runpod or vast.ai, rates for non secure GPUs are like a few cents an hour and datacenter GPUs not that expensive either.

P.S There are arbitrage opportunities you can spot using purely market data, but those are generally very short-term, don't warrant powerful models to detect and are pounced on by trading bots run by trading firms.

Nope, they're exactly the same as far as advancing human knowledge goes.

Lol what? That's the exact rationale "Open"AI used for not releasing the model weights for Dalle-2 (and instead selling it to Microsoft).