Hyperbolic Manifold Constrained Tabular Neural Network

arXiv:2607.09710 · cs.LG, stat.ML · Submitted 2026-06-23 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-06-23

Updated: 2026-09-28

Code: https://github.com/LAMDA-Tabular/TALENT

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 20× in this paper · explained in 2 episodes
• Embeddings — 20× in this paper · explained in 2 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Core — 14× in this paper · explained in 2 episodes
• XGBoost — 11× in this paper · explained in 3 episodes
• Hyperbolic Space — 8× in this paper · explained in 2 episodes
• Composition — 5× in this paper · explained in 2 episodes
• Hierarchical Structure — 5× in this paper · explained in 3 episodes
• Inductive Bias — 5× in this paper · explained in 3 episodes
• Representation Learning — 5× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Ablation Study — 4× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Hyperbolic Decision Embedding — 4× in this paper
• Feature Importance — 3× in this paper · explained in 2 episodes
• Geometric Mismatch — 3× in this paper · explained in 2 episodes
• Structured Reasoning — 3× in this paper · explained in 3 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 6 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 12 episodes
• Distribution Shifts — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Granularity Mismatch — 1× in this paper · explained in 2 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Linear Complexity — 1× in this paper · explained in 2 episodes
• Parameterization — 1× in this paper · explained in 2 episodes
• Structural Properties — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Weight Decay — 1× in this paper · explained in 2 episodes

Sources

• ExcelFormer: A neural network surpassing GBDTs on tabular data
• TabTransformer: Tabular Data Modeling Using Contextual Embeddings

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