Alexandre Gramfort

Cognitive Neuroscience · 2010–2023

A researcher in the Science corpus: 16 papers; trusts 107 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 177 citations into it; #4,760 of 10,852 by trust here, #953 by citations
• Artificial Intelligence — 109 citations into it; #25,981 of 35,345 by trust here, #408 by citations
• Computer Vision and Pattern Recognition — 74 citations into it; #20,888 of 21,575 by trust here, #13,542 by citations
• Signal Processing — 56 citations into it
• Optimization & Training — 34 citations into it
• Radiology, Nuclear Medicine and Imaging — 30 citations into it
• Natural Language Processing — 27 citations into it
• Statistics and Probability — 24 citations into it

Whom Alexandre Gramfort trusts overall

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

1. Arnaud Delorme
2. Geoffrey E. Hinton
3. Aapo Hyvärinen
4. Robert Oostenveld
5. Yann LeCun
6. Diederik P. Kingma
7. Yoshua Bengio
8. Alex Krizhevsky
9. Kaiming He
10. Bruce Fischl

Who trusts Alexandre Gramfort overall

1. Gaël Varoquaux
2. Charlotte Caucheteux
3. Matthias Feurer
4. Bertrand Thirion
5. Denis-Alexander Engemann
6. Mehdi Rahim
7. Niloufar Zebarjadi
8. Elisa Magosso
9. Mainak Jas
10. Tijl Grootswagers

Papers from Alexandre Gramfort's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. 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
• On the Consistency of Ordinal Regression Methods (2014) — 83 citations
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017) — 565 citations
• Evidence of a predictive coding hierarchy in the human brain listening to speech (2023) — 302 citations
• POT: Python Optimal Transport (2021) — 1,118 citations
• Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research (2020)
• Deep learning-based electroencephalography analysis: a systematic review (2019) — 1,358 citations
• MEG-BIDS, the brain imaging data structure extended to magnetoencephalography (2018)
• Seeing it all: Convolutional network layers map the function of the human visual system (2017) — 376 citations
• Autoreject: Automated artifact rejection for MEG and EEG data (2016) — 526 citations
• Machine learning for neuroimaging with scikit-learn (2014) — 2,159 citations
• MNE software for processing MEG and EEG data (2013) — 1,925 citations
• MEG and EEG data analysis with MNE-Python (2013) — 3,690 citations
• Identifying Predictive Regions from fMRI with TV-L1 Prior (2013) — 87 citations
• A supervised clustering approach for fMRI-based inference of brain states (2011) — 130 citations
• Brain covariance selection: better individual functional connectivity models using population prior (2010) — 307 citations