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Found 8 papers, 2 papers with code
Large language models surpass human experts in predicting neuroscience results
LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection
Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to the widely used cross-validation (CV) method.
On Leakage in Machine Learning Pipelines
Machine learning (ML) provides powerful tools for predictive modeling.
Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models
We created julearn, an open-source Python library, that allow researchers to design and evaluate complex ML pipelines without encountering in common pitfalls.
Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine.
Predictive Data Calibration for Linear Correlation Significance Testing
Inferring linear relationships lies at the heart of many empirical investigations.
A Too-Good-to-be-True Prior to Reduce Shortcut Reliance
Furthermore, an LCN's predictions can be used in a two-stage approach to encourage a high-capacity network (HCN) to rely on deeper invariant features that should generalize broadly.
Optimal Teaching for Limited-Capacity Human Learners
We apply a machine teaching procedure to a cognitive model that is either limited capacity (as humans are) or unlimited capacity (as most machine learning systems are).
