Gabor Gulyas

I struggle to figure out how to handle _n_ (trials) in the metric/d_score. The number of trials in BetaBinomial should come from the dataset, therefore it's not a parameter, but...

 

well, the idea is even simpler, the reshape added some randomness, here is the correct representation 

@stefanwebb no, I wasn't aware of the function name, thanks for sharing! I'll check and get back if it doesn't work, thank you!

You're right - stupid me. Although the solution for any function will be very convoluted. Let me put something together before closing this issue.

I hope this correct, but even if I made a mistake everyone gets the point. Doable, but not easy :) $$ x_4(x_1+x_2+x_3) $$ model = KAN(width=[4,2,1,1], grid=6, k=3) model.train(dataset, opt="Adam",...

I wanted to build an index-based chatbot, where the user can continuously ask about the document, in the style of GPTSimpleVectorIndex index = GPTSimpleVectorIndex.load_from_disk('index.json') response = index.query(text, response_mode="compact") is that...

absolutely, I did it, but I wanted to use chatGPT directly, instead of building up one - I thought it might create conversations that I can drive more directly. Anyway,...

your model is too complicated, there are gradient issues if the equation is too large. Try smaller, fewer input variables, smaller function, look at the result and get an understanding...

Although I'm happy with the link you gave, I'm sure pykan is not just a toy. I'm not the author, but you'r conclusion was based on my personal understanding. So...