Learning notes on Tabular Data.
- https://www.tabularmusings.com/posts/feature-interactions-dcnv2/ Redek
- https://sebastianraschka.com/blog/2022/deep-learning-for-tabular-data.html
- Tabular Data: Deep Learning is Not All You Need: https://arxiv.org/abs/2106.03253
- Declarative Machine Learning Systems: https://arxiv.org/abs/2107.08148
- Why every GBDT speed benchmark is wrong: https://openreview.net/pdf?id=ryexWdLRtm
- Why do tree-based models still outperform deep learning on tabular data?
- https://nn.labml.ai/ Annotated PyTorch Paper Implementations
- pytorch-widedeep, deep learning for tabular data IV: Deep Learning vs LightGBM. A thorough comparison between DL algorithms and LightGBM for tabular data for classification and regression problems: https://jrzaurin.github.io/infinitoml/2021/05/28/pytorch-widedeep_iv.html
- SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training: https://github.com/somepago/saint (repo)
- SAINT paper: https://arxiv.org/abs/2106.01342
- Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data https://arxiv.org/abs/2106.11189
- Revisiting Deep Learning Models for Tabular Data: https://arxiv.org/abs/2106.11959v1
- TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data: https://arxiv.org/abs/2005.08314v1
- Gradient Boosting Neural Networks: GrowNet. Paper: https://arxiv.org/abs/2002.07971, Code: https://github.com/sbadirli/GrowNet
- Structured data learning with TabTransformer: https://keras.io/examples/structured_data/tabtransformer/
- Deep Neural Networks and Tabular Data: A Survey https://arxiv.org/abs/2110.01889
Techniques:
Repos:
- 1 . https://github.com/radekosmulski/NVTabular
- 2 . https://github.com/tunguz/TabularBenchmarks (Tabular Bench Marking Repo)