GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
cs.LG
Submitted: 2026-10-06
Updated: 2026-10-06
Code: https://github.com/BoevaLab/GeneICL
Terminology
Sources
- What learning algorithm is in-context learning? Investigations with linear models
- Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models
- SurvPFN: Towards Foundation Models for Survival Predictions
- Universal Transformers
- Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
- TabPFN-3: Technical Report
- Tabular Foundation Models Can Do Survival Analysis
- Auto-Encoding Variational Bayes
- TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts
- SurvFM enables tabular foundation models for right-censored survival prediction
- Transformers Can Do Bayesian Inference
- TabICLv2: A better, faster, scalable, and open tabular foundation model
- Measurement noise limits the advantage of nonlinear models over linear models in biomedical prediction
- Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks
- Towards Evaluating Data Priors for Tabular Foundation Models
- Staying Alive: Uncensored Survival Analysis with Tabular Foundation Models
- LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models
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