Gaël Varoquaux

Cognitive Neuroscience · 2010–2022

A researcher in the Science corpus: 24 papers; trusts 156 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

Cognitive Neuroscience — 253 citations into it; #3,734 of 10,852 by trust here, #1,245 by citations
Radiology, Nuclear Medicine and Imaging — 130 citations into it; #1,391 of 3,265 by trust here, #956 by citations
Artificial Intelligence — 122 citations into it; #5,926 of 35,345 by trust here, #318 by citations
Computer Vision and Pattern Recognition — 86 citations into it; #10,786 of 21,575 by trust here, #8,722 by citations
Statistics and Probability — 52 citations into it
Evaluation & Benchmarks — 38 citations into it; #2,328 of 3,147 by trust here, #44 by citations
Biophysics — 30 citations into it
Molecular Biology — 24 citations into it

Whom Gaël Varoquaux trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Gaël Varoquaux overall

Papers from Gaël Varoquaux's perspective

Ordered by how strongly the researchers Gaël Varoquaux trusts endorse each paper. Same corpus, a different order for every researcher.

ImageNet classification with deep convolutional neural networks
Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Gradient-based learning applied to document recognition
ImageNet: A large-scale hierarchical image database
Long Short-Term Memory
Attention is All you Need (Neural Information Processing Systems)
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Learning Multiple Layers of Features from Tiny Images
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

Scikit-learn: Machine Learning in Python (2011) — 91,720 citations
The NumPy Array: A Structure for Efficient Numerical Computation (2011) — 7,422 citations
Why do tree-based models still outperform deep learning on tabular data? (2022) — 618 citations
Establishment of Best Practices for Evidence for Prediction A Review (2019) — 681 citations
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments (2016) — 1,663 citations
Why do tree-based models still outperform deep learning on typical tabular data? (2022) — 1,782 citations
Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019) — 312 citations
Relating Brain Structures To Open-Ended Descriptions Of Cognition (2017) — 1,120 citations
Seeing it all: Convolutional network layers map the function of the human visual system (2017) — 376 citations
Cross‐validation failure: Small sample sizes lead to large error bars (2017) — 697 citations
BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods (2016) — 311 citations
Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example (2016) — 654 citations
Predicting brain-age from multimodal imaging data captures cognitive impairment (2016) — 505 citations
Assessing and tuning brain decoders: Cross‐validation, caveats, and guidelines (2016) — 748 citations
Integrating Multimodal Priors in Predictive Models for the Functional Characterization of Alzheimer's Disease (2015) — 11 citations
Scikit-learn: Machine Learning Without Learning the Machinery (2015) — 507 citations
Which fMRI clustering gives good brain parcellations? (2014) — 347 citations
NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain (2014) — 592 citations
Machine learning for neuroimaging with scikit-learn (2014) — 2,159 citations