KMLP: A Scalable Hybrid Architecture for Web-Scale Tabular Data Modeling

• URL: http://arxiv.org/abs/2602.22777v1
• Date: Thu, 26 Feb 2026 09:12:12 GMT
• Title: KMLP: A Scalable Hybrid Architecture for Web-Scale Tabular Data Modeling
• Authors: Mingming Zhang, Pengfei Shi, Zhiqing Xiao, Feng Zhao, Guandong Sun, Yulin Kang, Ruizhe Gao, Ningtao Wang, Xing Fu, Weiqiang Wang, Junbo Zhao,
• Abstract summary: We introduce KMLP, a hybrid deep architecture integrating a shallow Kolmogorov-Arnold Network (KAN) front-end with a Gated Multilayer Perceptron (gMLP) backbone.<n> Experiments on public benchmarks and an industrial dataset with billions of samples show KMLP achieves state-of-the-art performance, with advantages over baselines like GBDTs increasing at larger scales.
• Score: 29.821326024794953
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Predictive modeling on web-scale tabular data with billions of instances and hundreds of heterogeneous numerical features faces significant scalability challenges. These features exhibit anisotropy, heavy-tailed distributions, and non-stationarity, creating bottlenecks for models like Gradient Boosting Decision Trees and requiring laborious manual feature engineering. We introduce KMLP, a hybrid deep architecture integrating a shallow Kolmogorov-Arnold Network (KAN) front-end with a Gated Multilayer Perceptron (gMLP) backbone. The KAN front-end uses learnable activation functions to automatically model complex non-linear transformations for each feature, while the gMLP backbone captures high-order interactions. Experiments on public benchmarks and an industrial dataset with billions of samples show KMLP achieves state-of-the-art performance, with advantages over baselines like GBDTs increasing at larger scales, validating KMLP as a scalable deep learning paradigm for large-scale web tabular data.

Related papers

• iLTM: Integrated Large Tabular Model [41.81329403540607]
  iLTM is an integrated Large Tabular Model that unifies tree-derived embeddings, dimensionality-agnostic representations, a meta-trained hypernetwork, multilayer perceptrons, and retrieval within a single architecture.
  arXiv  Detail & Related papers  (2025-11-20T00:20:16Z)
• Instruction-Guided Autoregressive Neural Network Parameter Generation [49.800239140036496]
  We propose IGPG, an autoregressive framework that unifies parameter synthesis across diverse tasks and architectures.<n>By autoregressively generating neural network weights' tokens, IGPG ensures inter-layer coherence and enables efficient adaptation across models and datasets.<n>Experiments on multiple datasets demonstrate that IGPG consolidates diverse pretrained models into a single, flexible generative framework.
  arXiv  Detail & Related papers  (2025-04-02T05:50:19Z)
• SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation [81.36747103102459]
  Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications.<n>Current state-of-the-art methods focus on training innovative architectural designs on confined datasets.<n>We investigate the impact of scaling up EHPS towards a family of generalist foundation models.
  arXiv  Detail & Related papers  (2025-01-16T18:59:46Z)
• Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models [83.65386456026441]
  Data-Juicer 2.0 is a data processing system backed by 100+ data processing operators spanning text, image, video, and audio modalities.<n>It supports more critical tasks including data analysis, synthesis, annotation, and foundation model post-training.<n>The system is publicly available and has been widely adopted in diverse research fields and real-world products such as Alibaba Cloud PAI.
  arXiv  Detail & Related papers  (2024-12-23T08:29:57Z)
• Beyond Tree Models: A Hybrid Model of KAN and gMLP for Large-Scale Financial Tabular Data [28.34587057844627]
  TKGMLP is a hybrid network for tabular data that combines shallow Kolmogorov Arnold Networks with Gated Multilayer Perceptron.<n>We validate TKGMLP on a real-world credit scoring dataset, where it achieves state-of-the-art results and outperforms current benchmarks.<n>We propose a novel feature encoding method for numerical data, specifically designed to address the predominance of numerical features in financial datasets.
  arXiv  Detail & Related papers  (2024-12-03T02:38:07Z)
• A Survey on Deep Tabular Learning [0.0]
  Tabular data presents unique challenges for deep learning due to its heterogeneous nature and lack of spatial structure. This survey reviews the evolution of deep learning models for Tabular data, from early fully connected networks (FCNs) to advanced architectures like TabNet, SAINT, TabTranSELU, and MambaNet.
  arXiv  Detail & Related papers  (2024-10-15T20:08:08Z)
• Pretraining Billion-scale Geospatial Foundational Models on Frontier [0.16492989697868893]
  Foundation Models (FMs) are trained with internet-scale unlabeled data via self-supervised learning. We investigate billion scale FMs and HPC training profiles for geospatial applications by pretraining on publicly available data. Our larger 3B parameter size model achieves up to 30% improvement in top1 scene classification accuracy.
  arXiv  Detail & Related papers  (2024-04-17T19:16:32Z)
• FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis [0.7751705157998379]
  The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP. Model soups averages multiple fine-tuned models aiming to improve performance on In-Domain (ID) tasks and enhance robustness against Out-of-Distribution (OOD) datasets. We propose a hierarchical merging approach that involves local and global aggregation of models at various levels.
  arXiv  Detail & Related papers  (2024-03-20T06:48:48Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities [69.16656086708291]
  Diffusion Probabilistic Field (DPF) models the distribution of continuous functions defined over metric spaces. We propose a new model comprising of a view-wise sampling algorithm to focus on local structure learning. The model can be scaled to generate high-resolution data while unifying multiple modalities.
  arXiv  Detail & Related papers  (2023-05-24T03:32:03Z)
• Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference [55.35176938713946]
  We develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its multi-stochastic-layer generative network. We propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a downward generative model. The efficacy and scalability of our models are demonstrated on both unsupervised and supervised learning tasks on big corpora.
  arXiv  Detail & Related papers  (2020-06-15T22:22:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.