Why do you call TabPFN a revolution for data science?
Industrial AI Podcast
English - March 01, 2023 23:01 - 49 minutes - 58.1 MBTechnology machine learning maschinenbau industrie künstliche intelligenz industrial ai artificial intelligence data ai podcast siemens Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
Prof. Dr. Frank Hutter and his team presented a "radically new approach" to tabular classification. It is a new tabular data classification method that takes < 1 second & yields SOTA performance (competitive with the best AutoML pipelines in an hour).
So far, it is limited in scale, though: it can only tackle problems up to 1000 training examples, 100 features and 10 classes. It works best when all features are numerical and there are no missing values (but we believe when we focus on those cases we’ll also improve performance for them).
The podcast is growing and we want to keep growing. That's why our German-language podcast is now available in English. We are happy about new listeners.
We thank our new partner [Siemens ](https://new.siemens.com/global/en/products/automation/topic-areas/artificial-intelligence-in-industry.html)
News:
Facebooks Language Model: https://github.com/facebookresearch/llama
Arduino and ML: https://hackaday.com/2023/02/24/tiny-machine-learning-on-as-little-as-2-kb-of-ram/
Our guest: Prof. Dr. Frank [Hutter ](https://www.linkedin.com/in/frank-hutter-9190b24b/)
The TabPFN Paper: https://arxiv.org/abs/2207.01848
The Summary: https://www.automl.org/tabpfn-a-transformer-that-solves-small-tabular-classification-problems-in-a-second/
Prof. Dr. Frank Hutter is one of the fathers of the AutoML approach. Now he has followed up and promised a revolution. TabPFN is its name. What is it?
The podcast is growing and we want to keep growing. That's why our German-language podcast is now available in English. We are happy about new listeners.
We thank our new partner Siemens
News:
Facebooks Language Model: https://github.com/facebookresearch/llama
Arduino and ML: https://hackaday.com/2023/02/24/tiny-machine-learning-on-as-little-as-2-kb-of-ram/
Our guest: Prof. Dr. Frank Hutter
The TabPFN Paper: https://arxiv.org/abs/2207.01848
The Summary: https://www.automl.org/tabpfn-a-transformer-that-solves-small-tabular-classification-problems-in-a-second/