Acknowledgments

We would like to express our sincere gratitude to the following repositories and projects for providing helpful components, functions, and ideas that have contributed to the development of TALENT. These projects have significantly supported the integration of various methods into our toolbox:

• Rtdl-revisiting-models

• Rtdl-num-embeddings

• Tabular-dl-tabr

• DANet

• TabCaps

• DNNR

• PTaRL

• Saint

• SwitchTab

• TabNet

• TabPFN

• Tabtransformer-pytorch

• TANGOS

• GrowNet

• HyperFast

• BiSHop

• ProtoGate

• Pytabkit

• Excelformer

• GRANDE

• AMFormer

• TabM

• TabICL

• TabAutoPNPNet

Additionally, we would like to acknowledge the authors of these works for their invaluable contributions to the field of machine learning on tabular data. Without their innovations, TALENT would not have reached its current state.

If you find any components or resources in TALENT useful in your research, we encourage you to cite these repositories and papers alongside TALENT.