ersiees

It’s same price for lower latency.

Sub two hour marathon is a more recent example of this

Meta, please go back to publishing weights. Your models aren't top-tier, this wouldn't hurt you at all at your current position in the rankings. They wouldn't have the best open-weight models, but the best western open-weight models.

Isn’t it too late for that? Won’t that rather cement the oligopoly we have right now?

I think the European urinals already follow the proposed designs closely. They only compare to very old chunky designs.

I don’t love fennel, it usually dominates the whole taste of a dish for me

So, 100$ Nike shoes will soon be 125$.

Yes, there are normalizations applied before the features are fed to the neural network. Additionally, the neural network is trained on a very diverse set of artificial datasets.

Author here: The new introduction of attention between features did make a big impact compared to the first variant of TabPFN. The old model handled every feature like it was completely different to be feature 5 vs 15, but actually features are typically more-or-less permutation invariant. So the logic is similar to why a CNN is better for images than an MLP.

To put it very simply, the trick is that while the others train a new model for each problem, TabPFN is pre-trained to handle any kind of problem on the fly.

To draw a parallel to NLP: previously people trained a neural network for each kind of text classification they wanted to do, but then LLMs came around that pre-trained to learn to perform new tasks on the fly. Similarly, TabPFN learns to do new tasks on the fly just from the context (dataset) given.

Training and prediction in these models is by default one and the same, similar to how the prediction of the next token in an LLM is not split into learning from context and then doing the actual prediction. There is a way to split this even up, though, then the predictions, I believe, take something like 1/10s for medium-sized datasets.