1.9k citations · 2.5k across the 7 of their papers we have counts for
7 papers
Query-Level Uncertainty in Large Language Models
Lihu Chen, Gerard de Melo, Fabian M. Suchanek +1
It is important for Large Language Models (LLMs) to be aware of the boundary of their knowledge, distinguishing queries they can confidently answer from those that lie beyond their…
CARTE: Pretraining and Transfer for Tabular Learning
Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux
Pretrained deep-learning models are the go-to solution for images or text. However, for tabular data the standard is still to train tree-based models. Indeed, transfer learning on…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Learning Neural Representations of Human Cognition across Many fMRI Studies
Arthur Mensch, Julien Mairal, Danilo Bzdok +2
Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets. It opens the door to large-scale statistical models. Finding a unified perspective for…
API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck, Gilles Louppe, Mathieu Blondel +12
Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various…
Small-sample Brain Mapping: Sparse Recovery on Spatially Correlated Designs with Randomization and Clustering
Gael Varoquaux, Alexandre Gramfort, Bertrand Thirion
Functional neuroimaging can measure the brain?s response to an external stimulus. It is used to perform brain mapping: identifying from these observations the brain regions involve…