mlsanityData to 14 Oct 2026

Gradient Boosting Neural Networks: GrowNet

arXiv 2002.07971 ↗First posted 19 Feb 2020cs.LG stat.MLcited by 46

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Abstract (arXiv)

A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework with specific examples presented for classification, regression, and learning to rank. A fully corrective step is incorporated to remedy the pitfall of greedy function approximation of classic gradient boosting decision tree. The proposed model rendered outperforming results against state-of-the-art boosting methods in all three tasks on multiple datasets. An ablation study is performed to shed light on the effect of each model components and model hyperparameters.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Feb 2020.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 8; 2024: 14; 2025: 10; 2026: 4.

46 papers.

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  1. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  2. 2021Tabular Data: Deep Learning is Not All You Needcited by 535
  3. 2022On Embeddings for Numerical Features in Tabular Deep Learningcited by 186
  4. 2024TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensemblingcited by 113
  5. 2024ReconBoost: Boosting Can Achieve Modality Reconcilementcited by 60
  6. 2022Transfer Learning with Deep Tabular Modelscited by 52
  7. 2024TALENT: A Tabular Analytics and Learning Toolboxcited by 52
  8. 2021Boost then Convolve: Gradient Boosting Meets Graph Neural Networkscited by 44
  9. 2022Revisiting Pretraining Objectives for Tabular Deep Learningcited by 43
  10. 2025A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilitiescited by 41
  11. 2024SwitchTab: Switched Autoencoders Are Effective Tabular Learnerscited by 38
  12. 2025TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problemscited by 36
34 more
  1. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  2. 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 25
  3. 2023ReConTab: Regularized Contrastive Representation Learning for Tabular Datacited by 25
  4. 2023A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learningcited by 24
  5. 2022Precision Machine Learningcited by 14
  6. 2024Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domainscited by 12
  7. 2022Boosted Dynamic Neural Networkscited by 8
  8. 2024Fully Test-time Adaptation for Tabular Datacited by 8
  9. 2025On-device edge learning for IoT data streams: a surveycited by 8
  10. 2024Multimodal Clinical Trial Outcome Prediction with Large Language Modelscited by 7
  11. 2024TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Orderingcited by 5
  12. 2023Homological Convolutional Neural Networkscited by 4
  13. 2025Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learningcited by 4
  14. 2025Realistic Evaluation of TabPFN v2 in Open Environmentscited by 4
  15. 2024Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPscited by 3
  16. 2024Transformers with Stochastic Competition for Tabular Data Modellingcited by 3
  17. 2025Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboardcited by 3
  18. 2023A Gradient Boosting Approach for Training Convolutional and Deep Neural Networkscited by 2
  19. 2024Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learningcited by 2
  20. 2024TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Datacited by 2
  21. 2022Transfer learning for ensembles: reducing computation time and keeping the diversitycited by 1
  22. 2023Incomplete Multimodal Learning for Complex Brain Disorders Predictioncited by 1
  23. 2024Forecasting with Hyper-Treescited by 1
  24. 2026ARASH: Adaptive Retrieval And Shot Selection for Tabular Predictioncited by 1
  25. 2022Towards Domain-Independent Supervised Discourse Parsing Through Gradient Boostingcited by 0
  26. 2023Prediction of Post-Operative Renal and Pulmonary Complications Using Transformerscited by 0
  27. 2023I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiativescited by 0
  28. 2024Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainabilitycited by 0
  29. 2025Representation Learning on Out of Distribution in Tabular Datacited by 0
  30. 2025Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classificationcited by 0
  31. 2025HiPreNets: High-Precision Neural Networks through Progressive Trainingcited by 0
  32. 2026CFNN: Continued Fraction Neural Networkcited by 0
  33. 2026Gradient Boosting within a Single Attention Layercited by 0
  34. 2026TabNSM: Neural Sparse Mixer for Tabular Regressioncited by 0