Exploring accuracy and interpretability trade-off in tabular learning with novel attention-based models

Author:

Amekoe Kodjo MawuenaORCID,Azzag Hanane,Dagdia Zaineb Chelly,Lebbah Mustapha,Jaffre Gregoire

Funder

Groupe BPCE

ANRT - CIFRE

Publisher

Springer Science and Business Media LLC

Reference47 articles.

1. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30

2. Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp 785–794

3. Somepalli G, Goldblum M, Schwarzschild A, Bruss CB, Goldstein T (2021) Saint: Improved neural networks for tabular data via row attention and contrastive pre-training. arXiv preprint arXiv:2106.01342

4. Kossen J, Band N, Lyle C, Gomez AN, Rainforth T, Gal Y (2021) Self-attention between datapoints: going beyond individual input-output pairs in deep learning. Adv Neural Inf Process Syst 34:28742–28756

5. Huang X, Khetan A, Cvitkovic M, Karnin Z (2020) Tabtransformer: Tabular data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678

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