Today we’re talking to Noah Hollmann and Samuel Muller about their paper on TabPFN - which is an incredible spin on AutoML based on Bayesian inference and transformers.
[Quick note on audio quality]: Some of the tracks have not recorded perfectly but I felt that the content there was too important not to release. Sorry for any ear-strain!
In the episode, we spend some time discussing posterior predictive probabilities before discussing how exactly they’ve pre-fitted their network, how they got their training data, what the network looks like, and how the system is performing.
To give you a taste of it, on datasets up to 1,000 training instances and 100 features, it takes less than a second to train and predict a classifier!
Read their paper here: https://arxiv.org/pdf/2207.01848.pdf
Follow Samuel on Twitter, here: https://twitter.com/SamuelMullr
Follow Noah on Twitter, here: https://twitter.com/noahholl