TAFFY: A Task-Adaptive Tabular Foundation Model with In-Context Diversity
cs.LG
Submitted: 2026-10-06
Updated: 2026-10-06
Terminology
Sources
- Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models
- Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality
- Fine-tuned In-Context Learning Transformers are Excellent Tabular Data Classifiers
- AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
- Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
- Adaptive Computation Time for Recurrent Neural Networks
- TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
- TabPFN-3: Technical Report
- TabTransformer: Tabular Data Modeling Using Contextual Embeddings
- Interpretable Tabular Foundation Models via In-Context Kernel Regression
- TabICLv2: A better, faster, scalable, and open tabular foundation model
- Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
- LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models
- LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence
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