REVIEW 5 cited by
Revisiting Deep Learning Models for Tabular Data
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed models are usually not properly compared to each other and existing works often use different benchmarks and experiment protocols. As a result, it is unclear for both researchers and practitioners what models perform best. Additionally, the field still lacks effective baselines, that is, the easy-to-use models that provide competitive performance across different problems. In this work, we perform an overview of the main families of DL architectures for tabular data and raise the bar of baselines in tabular DL by identifying two simple and powerful deep architectures. The first one is a ResNet-like architecture which turns out to be a strong baseline that is often missing in prior works. The second model is our simple adaptation of the Transformer architecture for tabular data, which outperforms other solutions on most tasks. Both models are compared to many existing architectures on a diverse set of tasks under the same training and tuning protocols. We also compare the best DL models with Gradient Boosted Decision Trees and conclude that there is still no universally superior solution.
Forward citations
Cited by 5 Pith papers
-
PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks
PatTree is a new graph-based patient representation that reports 98.5% balanced accuracy on ADNI three-class classification, but the estimate is likely inflated by test-set selection and supervised feature extraction.
-
Modeling Decisions in Blockchain Analytics: A Leakage-Aware Evaluation of Tree-Based vs. Sequential Models
The paper reports that XGBoost beats Transformer and BiLSTM models for Ethereum actor classification after masking certain high-signal contracts, and that sequence order adds little signal.
-
Mixing Configurations for Downstream Prediction
Mixing multiple resolution clusterings of an embedding, aligned between train and test, and fused by attention, improves downstream regression and classification over single-resolution baselines.
-
Calibrated Tree-Neural Fusion for Fine-Grained Vegetation Community Classification
Calibrated EcoTreeFuseNet-Plus matches ExtraTrees on 29-class vegetation labels while cutting expected calibration error from 0.39 to 0.07 via temperature scaling and leakage-aware stacking.
-
Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification
Across 12 fixed-hyperparameter tabular benchmarks, KANs beat MLPs on 9/12 datasets in accuracy and 10/12 in F1, yet cost ~16x parameters; the paper's significance tests are internally inconsistent.
Discussion (0). Sign in to comment.