# TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023

arXiv: https://arxiv.org/abs/2307.14338 · HTML: https://mlsanity.com/paper/2307.14338/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 53 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [Attentive Neural Processes](https://mlsanity.com/paper/1901.05761/) (2019)
- [Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data](https://mlsanity.com/paper/1909.06312/) (2019)
- [On Embeddings for Numerical Features in Tabular Deep Learning](https://mlsanity.com/paper/2203.05556/) (2022)
- [SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption](https://mlsanity.com/paper/2106.15147/) (2021)
- [Well-tuned Simple Nets Excel on Tabular Datasets](https://mlsanity.com/paper/2106.11189/) (2021)
- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning](https://mlsanity.com/paper/2106.02584/) (2021)
- [Case-based Reasoning for Natural Language Queries over Knowledge Bases](https://mlsanity.com/paper/2104.08762/) (2021)
- [Retrieval Augmented Classification for Long-Tail Visual Recognition](https://mlsanity.com/paper/2202.11233/) (2022)
- [Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks](https://mlsanity.com/paper/2107.07455/) (2021)
- [Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data](https://mlsanity.com/paper/2203.08773/) (2022)
- [The Tree Ensemble Layer: Differentiability meets Conditional Computation](https://mlsanity.com/paper/2002.07772/) (2020)

## Built on by (most cited first, 30 of 74)

- [TabDPT: Scaling Tabular Foundation Models on Real Data](https://mlsanity.com/paper/2410.18164/) (2024)
- [TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling](https://mlsanity.com/paper/2410.24210/) (2024)
- [Retrieval & Fine-Tuning for In-Context Tabular Models](https://mlsanity.com/paper/2406.05207/) (2024)
- [TALENT: A Tabular Analytics and Learning Toolbox](https://mlsanity.com/paper/2407.04057/) (2024)
- [A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities](https://mlsanity.com/paper/2502.17361/) (2025)
- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [ConTextTab: A Semantics-Aware Tabular In-Context Learner](https://mlsanity.com/paper/2506.10707/) (2025)
- [TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications](https://mlsanity.com/paper/2311.02971/) (2023)
- [TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks](https://mlsanity.com/paper/2406.19380/) (2024)
- [PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning](https://mlsanity.com/paper/2404.00776/) (2024)
- [Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later](https://mlsanity.com/paper/2407.03257/) (2024)
- [Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning](https://mlsanity.com/paper/2511.02818/) (2025)
- [A Survey on Deep Tabular Learning](https://mlsanity.com/paper/2410.12034/) (2024)
- [TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields](https://mlsanity.com/paper/2505.18125/) (2025)
- [TabFlex: Scaling Tabular Learning to Millions with Linear Attention](https://mlsanity.com/paper/2506.05584/) (2025)
- [EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models](https://mlsanity.com/paper/2404.12404/) (2024)
- [T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data](https://mlsanity.com/paper/2410.05016/) (2024)
- [On Finetuning Tabular Foundation Models](https://mlsanity.com/paper/2506.08982/) (2025)
- [Exploring Fine-Tuning for Tabular Foundation Models](https://mlsanity.com/paper/2601.09654/) (2026)
- [Fully Test-time Adaptation for Tabular Data](https://mlsanity.com/paper/2412.10871/) (2024)
- [Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer](https://mlsanity.com/paper/2502.04573/) (2025)
- [LLM Meeting Decision Trees on Tabular Data](https://mlsanity.com/paper/2505.17918/) (2025)
- [Chunked TabPFN: Exact Training-Free In-Context Learning for Long-Context Tabular Data](https://mlsanity.com/paper/2509.00326/) (2025)
- [LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence](https://mlsanity.com/paper/2609.17488/) (2026)
- [TabGemma: Text-Based Tabular ICL via LLM using Continued Pretraining and Retrieval](https://mlsanity.com/paper/2511.03570/) (2025)
- [Retrieve, Merge, Predict: Augmenting Tables with Data Lakes](https://mlsanity.com/paper/2402.06282/) (2024)
- [Unveiling Multiple Descents in Unsupervised Autoencoders](https://mlsanity.com/paper/2406.11703/) (2024)
- [Scaling Up Diffusion and Flow-based XGBoost Models](https://mlsanity.com/paper/2408.16046/) (2024)
- [Understanding the Limits of Deep Tabular Methods with Temporal Shift](https://mlsanity.com/paper/2502.20260/) (2025)
- [Retrieval Augmented Time Series Forecasting](https://mlsanity.com/paper/2505.04163/) (2025)

## Abstract (arXiv)

Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers. However, despite the recent efforts, the non-DL algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution for these problems. One of the research directions aimed at improving the position of tabular DL involves designing so-called retrieval-augmented models. For a target object, such models retrieve other objects (e.g. the nearest neighbors) from the available training data and use their features and labels to make a better prediction. In this work, we present TabR -- essentially, a feed-forward network with a custom k-Nearest-Neighbors-like component in the middle. On a set of public benchmarks with datasets up to several million objects, TabR marks a big step forward for tabular DL: it demonstrates the best average performance among tabular DL models, becomes the new state-of-the-art on several datasets, and even outperforms GBDT models on the recently proposed "GBDT-friendly" benchmark (see Figure 1). Among the important findings and technical details powering TabR, the main ones lie in the attention-like mechanism that is responsible for retrieving the nearest neighbors and extracting valuable signal from them. In addition to the much higher performance, TabR is simple and significantly more efficient compared to prior retrieval-based tabular DL models.