arXiv Machine Learning August 18, 2026 By Manish Kumar, Anton Frederik Thielmann, Christoph Weisser, Benjamin S\"afken

From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning

Read the original on arXiv Machine Learning →

arXiv:2604. 05635v2 Announce Type: replace Abstract: Numerical preprocessing remains a critical component of tabular deep learning, as the representation of continuous features can strongly affect downstream performance.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

llms

arXiv AI

Aug 11

Tabular Numeric Stretch Transformation

arXiv:2608. 09162v1 Announce Type: cross Abstract: Tabular data presents unique challenges for deep learning due to its heterogeneous nature, where numeric features exhibit diverse distributions, scales, and statistical properties.

By Zihao Ye, Juyong Kim, Johnna Sundberg, Burak Varici, Pradeep Ravikumar

benchmarks

arXiv AI

Jun 19

When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning

arXiv:2606. 19827v1 Announce Type: cross Abstract: Medical tabular data are ubiquitous in clinical research, but deep learning for tables remains underexplored because reliable labels often require costly expert adjudication, even though structured clinical variables are routinely available in tabular form.

By Daehwan Kim, Haejun Chung, Ikbeom Jang

fine-tuning benchmarks safety

arXiv Machine Learning

Jun 3

Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE)

arXiv:2605. 18629v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) are one of the main methods to interpret the inner workings of deep neural networks (DNNs), decomposing activations into higher-dimensional features.

By Micha{\l} Brzozowski, Neo Christopher Chung

efficiency benchmarks safety

arXiv Machine Learning

Jun 2

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.

By Andrej Tschalzev, Nick Erickson, Yuyang Wang, Huzefa Rangwala, Stefan L\"udtke, Heiner Stuckenschmidt, Christian Bartelt

benchmarks

arXiv AI

Jun 9

TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders

arXiv:2606. 09323v1 Announce Type: new Abstract: Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals.

By Wei Pang, Xiangru Jian, Hehan Li, Zhixuan Yu, Alex Xue, Jinyang Li, Zhengyuan Dong, Xinjian Zhao, Hao Xu, Chao Zhang, Reynold Cheng, M. Tamer \"Ozsu, Tianshu Yu

rag benchmarks

arXiv Machine Learning

Jul 14

TabLoRA: Parameter-Efficient Low-Rank Ensemble Learning for Large-Scale Tabular Data

arXiv:2607. 10077v1 Announce Type: new Abstract: Tabular learning is still dominated by gradient-boosted decision trees (GBDTs), while recent deep learning approaches have become increasingly competitive.

By Jiaqi Luo, Shixin Xu

benchmarks

Related stories

Tabular Numeric Stretch Transformation

When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning

Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE)

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders

TabLoRA: Parameter-Efficient Low-Rank Ensemble Learning for Large-Scale Tabular Data