Open Access
| Issue |
ITM Web Conf.
Volume 88, 2026
The 2026 International Conference on Artificial Intelligence, Big Data and Computer Science (AIBDCS 2026)
|
|
|---|---|---|
| Article Number | 01036 | |
| Number of page(s) | 7 | |
| Section | Artificial Intelligence, Big Data and Computer Science | |
| DOI | https://doi.org/10.1051/itmconf/20268801036 | |
| Published online | 27 July 2026 | |
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. [Google Scholar]
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems. [Google Scholar]
- Huang, X., Khetan, A., Cvitkovic, M., & Karnin, Z. (2020). TabTransformer: Tabular data modeling using contextual embeddings. arXiv preprint, arXiv:2012.06678. [Google Scholar]
- Gorishniy, Y., Rubachev, I., Khrulkov, V., & Babenko, A. (2021). Revisiting deep learning models for tabular data. Advances in Neural Information Processing Systems. [Google Scholar]
- Hollmann, N., Müller, S., Eggensperger, K., & Hutter, F. (2023). TabPFN: A transformer that solves small tabular classification problems in a second. International Conference on Learning Representations. [Google Scholar]
- Badaro, G., Saeed, M., & Papotti, P. (2023). Transformers for tabular data representation: A survey of models and applications. Transactions of the Association for Computational Linguistics, 11, 227–249. [Google Scholar]
- Fang, X., Xu, W., Tan, F. A., Zhang, J., Hu, Z., Qi, Y., Nickleach, S., Socolinsky, D., Sengamedu, S., & Faloutsos, C. (2024). Large language models on tabular data: Prediction, generation, and understanding – A survey. Transactions on Machine Learning Research. [Google Scholar]
- Akhtar, M., Suri, N., et al. (2023). Exploring the numerical reasoning capabilities of language models. Findings of the Association for Computational Linguistics: EMNLP. [Google Scholar]
- Golkar, S., Pettee, M., Eickenberg, M., Bietti, A., Cranmer, M., Krawezik, G., Lanusse, F., McCabe, M., Ohana, R., Parker, L., et al. (2023). xVal: A continuous numerical tokenization for scientific language models. arXiv preprint, arXiv:2310.02989. [Google Scholar]
- Scikit-learn Developers. (2024). QuantileTransformer. Scikit-learn Documentation. [Google Scholar]
- Peterson, R. A. (2021). Ordered quantile normalization: A semiparametric transformation built for the cross-validation era. Journal of Applied Statistics, 48(13-15), 2312–2327. [Google Scholar]
- Cortez, P., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2009). Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems, 47(4), 547–553. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.