If you use an evolutionary algorithm like NEAT what is the selection criteria?

Sorry I don't think I understand your question.

I guess the point of the reward model is to approximate human feedback and instead of hiring humans to actually rank (e.g.) 1billion chats needed to update the LLM, train a reward model with 1% of them then use it to simulate human evaluators 99% of the times.

One possibility is GPU acceleration using the cuML framework, but if you are must use a specific framework like sklearn it won't be feasible. https://medium.com/rapids-ai/accelerating-random-forests-up-to-45x-using-cuml-dfb782a31bea

There are some alternatives such as Google, AWS, Gradient, you may be able to get student credits. Also, even if you don't need a GPU, you can rent an instances with many CPU cores at Vast.ai for cheap (even with the GPU it's cheaper than a CPU only AWS instance with the same amount of cores), for example the cheapest instance with 16vCPU is < $0.20/hour and only needs a credit card. The main issue with vast.ai is that you should save your results before shutting down the instance because they are tied to the machine which may become unavailable.

So what they (it?) evolve for?

Found relevant code at https://github.com/marsggbo/automl_a_survey_of_state_of_the_art + all code implementations here

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Hey, I dove into "Progressive Growing of GANs" without knowing what weights were. And now here I am, four or five years later. I've trained my own classifiers based on ViTs, DNNs, written python interfaces for them, and I'm working on tooling to make Automatic1111's GUI behave better with Stable Diffusion. We've all got to start somewhere.

When they originally went closed-source they claimed it was because of the dangers that being open-sourced presented.

About a year later they dropped their non-profit status and sold out to Microsoft.

Love the company, but that's some crazy double speak there.

Not exactly. I have millions of points, most of which are not related to my query vectors. I want to iteratively refine my search: search, mark results as "relevant" or "irrelevant", repeat search with updated query.

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Thanks for your interest. If you open an issue on GitHub about this, I will keep it in mind as a reminder, and I can share pre-trained weights at the appropriate time.

It was my grain of sand, self attention is a bunch of matrix multiplications. 12 layers of the same, it makes sense to understand why QK^t. If the question would have been how to understand maskrcnn the answer would have been different.

Edit: 12 layers in ViT base / BERT base

They just need to call it "money hype train, we will fix it as we go." If a company ran that honest PR campaign I'd be a customer.

CLIPPY MAKES HIS GLORIOUS RETURN!!?!?!!!!

ALL HAIL CLIPPY THE AI SENTIENT SUPER GOD

I got a bunch of credits from cloud hosting providers haha. Also since this is a beta I wanted a generous free tier. To connect with banana.dev, you would need to sign up for your own account and pass in your API key to the Python function that’s getting run on cakework. Thin

Wow! How do you manage to say afloat?

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We are spinning up the serverless gpu hosting the model using banana.dev btw (which I’ve really liked so far). Cakework spins up CPU-only microVMs for now, since the Firecracker virtual machine monitor runs only on CPUs.

You have a sign up!

Yup, that’s one of our examples! You can run this project to run a stable diffusion model on a serverless GPU: https://github.com/usecakework/cakework/tree/main/examples/image_generation

I was going to recommend this book. You beat me to it.

Stable diffusion?

That is fascinating work.

I'd like to read the paper and will, given the time; are the results promising?

It seems reasonable that a graph with a small branching factor could reasonably replicate logarithmic search complexity of the input space to at least some extent; I'm very interested in exploring this space.

🙌 heck yeah!