1 citations · 1 across the 5 of their papers we have counts for
6 papers
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings
Félix Lefebvre, David Holzmüller, Gioia Blayer +3
Real-world tables are often heterogeneous, combining numerical features with free-form string columns. Table foundation models (TFMs) excel on numerical tables but require strings…
Exploring Differences Between Tabular Enterprise Data and Public Benchmarks
Myung Jun Kim, Maximilian Schambach, Frank Essenberger +2
Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks. Yet, little is known for enterprise data, w…
STRABLE: Benchmarking Tabular Machine Learning with Strings
Gioia Blayer, Myung Jun Kim, Félix Lefebvre +8
Benchmarking tabular learning has revealed the benefit of dedicated architectures, pushing the state of the art. But real-world tables often contain string entries, beyond numbers,…
Table Foundation Models: on knowledge pre-training for tabular learning
Myung Jun Kim, Félix Lefebvre, Gaëtan Brison +2
Table foundation models bring high hopes to data science: pre-trained on tabular data to embark knowledge or priors, they should facilitate downstream tasks on tables. One specific…
CARTE: Pretraining and Transfer for Tabular Learning
Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux
Pretrained deep-learning models are the go-to solution for images or text. However, for tabular data the standard is still to train tree-based models. Indeed, transfer learning on…
Vectorizing string entries for data processing on tables: when are larger language models better?
Léo Grinsztajn, Edouard Oyallon, Myung Jun Kim +1
There are increasingly efficient data processing pipelines that work on vectors of numbers, for instance most machine learning models, or vector databases for fast similarity searc…