Jon Krohn talks tabular data with Frank Hutter, Professor of Artificial Intelligence at Universität Freiburg in Germany. Despite the great steps that deep learning has made in analysing images, audio, and natural language, tabular data has remained its insurmountable obstacle. In this episode, Frank Hutter details the path he has found around this obstacle even with limited data by using a ground-breaking transformer architecture. Named TabPFN, this approach is vastly outperforming other architectures, as testified by a write up of TabPFN’s capabilities in Nature. Frank talks about his work on version 2 of TabPFN, the architecture’s cross-industry applicability, and how TabPFN is able to return accurate results with synthetic data.
This episode is brought to you by ODSC, the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
(05:57) All about the TabPFN architecture
(21:27) Use cases for Bayesian inference
(35:07) On getting published in Nature
(44:03) How TabPFN handles time series data
(51:52) All about Prior Labs
Additional materials: www.superdatascience.com/863
863: TabPFN: Deep Learning for Tabular Data (That Actually Works!), with Prof. Frank Hutter | Super Data Science: ML & AI Podcast with Jon Krohn - Listen or read transcript on Metacast
