# LocalGLMnet: interpretable deep learning for tabular data

arXiv: https://arxiv.org/abs/2107.11059 · HTML: https://mlsanity.com/paper/2107.11059/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [Learning Deep Transformer Models for Machine Translation](https://mlsanity.com/paper/1906.01787/) (2019)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [LassoNet: A Neural Network with Feature Sparsity](https://mlsanity.com/paper/1907.12207/) (2019)
- [Explainable Neural Networks based on Additive Index Models](https://mlsanity.com/paper/1806.01933/) (2018)
- [Generative Synthesis of Insurance Datasets](https://mlsanity.com/paper/1912.02423/) (2019)
- [Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles](https://mlsanity.com/paper/2103.11706/) (2021)

## Built on by (most cited first, 15 of 15)

- [Enhancing Actuarial Non-Life Pricing Models via Transformers](https://mlsanity.com/paper/2311.07597/) (2023)
- [Interpretable Graph Neural Networks for Tabular Data](https://mlsanity.com/paper/2308.08945/) (2023)
- [The Credibility Transformer](https://mlsanity.com/paper/2409.16653/) (2024)
- [Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data](https://mlsanity.com/paper/2601.07675/) (2026)
- [Detection of Interacting Variables for Generalized Linear Models via Neural Networks](https://mlsanity.com/paper/2209.08030/) (2022)
- [A tree-based varying coefficient model](https://mlsanity.com/paper/2401.05982/) (2024)
- [Multiple Yield Curve Modeling and Forecasting using Deep Learning](https://mlsanity.com/paper/2401.16985/) (2024)
- [Distributional Refinement Network: Distributional Forecasting via Deep Learning](https://mlsanity.com/paper/2406.00998/) (2024)
- [Interpretable Graph Neural Networks for Heterogeneous Tabular Data](https://mlsanity.com/paper/2408.07661/) (2024)
- [MonoSparse-CAM: Efficient Tree Model Processing via Monotonicity and Sparsity in CAMs](https://mlsanity.com/paper/2407.11071/) (2024)
- [Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance](https://mlsanity.com/paper/2503.21321/) (2025)
- [An Interpretable Deep Learning Model for General Insurance Pricing](https://mlsanity.com/paper/2509.08467/) (2025)
- [Global Interpretability via Automated Preprocessing: A Framework Inspired by Psychiatric Questionnaires](https://mlsanity.com/paper/2602.23459/) (2026)
- [Is TabPFN the Silver Bullet for Insurance Pricing?](https://mlsanity.com/paper/2605.22892/) (2026)
- [Scaling Laws, Tabular Data and Actuarial Ratemaking Models](https://mlsanity.com/paper/2609.03106/) (2026)

## Abstract (arXiv)

Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models. The disadvantage of deep learning models is that their solutions are difficult to interpret and explain, and variable selection is not easily possible because deep learning models solve feature engineering and variable selection internally in a nontransparent way. Inspired by the appealing structure of generalized linear models, we propose a new network architecture that shares similar features as generalized linear models, but provides superior predictive power benefiting from the art of representation learning. This new architecture allows for variable selection of tabular data and for interpretation of the calibrated deep learning model, in fact, our approach provides an additive decomposition in the spirit of Shapley values and integrated gradients.