2 citations · 2 across the 9 of their papers we have counts for
11 papers · 1 filter
nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN
Alexander Pfefferle, Johannes Hog, Lennart Purucker +1
Tabular foundation models such as TabPFN have revolutionized predictive machine learning for tabular data. At the same time, the driving factors of this revolution are hard to unde…
Does TabPFN Understand Causal Structures?
Omar Swelam, Lennart Purucker, Jake Robertson +3
Causal discovery is fundamental for multiple scientific domains, yet extracting causal information from real world data remains a significant challenge. Given the recent success on…
DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
Julien Siems, Timur Carstensen, Arber Zela +3
Linear Recurrent Neural Networks (linear RNNs) have emerged as competitive alternatives to Transformers for sequence modeling, offering efficient training and linear-time inference…
Agentic NL2SQL to Reduce Computational Costs
Dominik Jehle, Lennart Purucker, Frank Hutter
Translating natural language queries into SQL queries (NL2SQL or Text-to-SQL) has recently been empowered by large language models (LLMs). Using LLMs to perform NL2SQL methods on a…
Quickly Tuning Foundation Models for Image Segmentation
Breenda Das, Lennart Purucker, Timur Carstensen +1
Foundation models like SAM (Segment Anything Model) exhibit strong zero-shot image segmentation performance, but often fall short on domain-specific tasks. Fine-tuning these models…
How Usable is Automated Feature Engineering for Tabular Data?
Bastian Schäfer, Lennart Purucker, Maciej Janowski +1
Tabular data, consisting of rows and columns, is omnipresent across various machine learning applications. Each column represents a feature, and features can be combined or transfo…