Bertrand Thirion
Cognitive Neuroscience · 2006–2019
A researcher in the Science corpus: 16 papers; trusts 64 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 220 citations into it; #2,640 of 10,852 by trust here, #1,063 by citations
- Radiology, Nuclear Medicine and Imaging — 97 citations into it; #1,327 of 3,265 by trust here, #594 by citations
- Statistics and Probability — 50 citations into it
- Computer Vision and Pattern Recognition — 42 citations into it; #20,893 of 21,575 by trust here, #14,478 by citations
- Artificial Intelligence — 38 citations into it; #25,980 of 35,345 by trust here, #410 by citations
- Experimental and Cognitive Psychology — 22 citations into it
- Signal Processing — 21 citations into it
- Evaluation & Benchmarks — 14 citations into it; #2,121 of 3,147 by trust here, #44 by citations
Whom Bertrand Thirion trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jianbo Shi
- Christopher K. I. Williams
- Andrew Y. Ng
- Barbara Olshausen
- Amir Shmuel
- David D. Cox
- Stella X. Yu
- Yukiyasu Kamitani
- Karl J. Friston
- Isabelle M Guyon
Who trusts Bertrand Thirion overall
- Brian A. Wandell
- Thomas Naselaris
- John-Dylan Haynes
- Alexandre Abraham
- Serge O. Dumoulin
- Gaël Varoquaux
- Tal Yarkoni
- Kamalaker Dadi
- Ender Konukoglu
- Mehdi Rahim
Papers from Bertrand Thirion's perspective
Ordered by how strongly the researchers Bertrand Thirion 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
Their papers
- Scikit-learn: Machine Learning in Python (2011) — 91,720 citations
- Variability in the analysis of a single neuroimaging dataset by many teams (2019) — 901 citations
- Best Practices in Data Analysis and Sharing in Neuroimaging using MRI (2016) — 486 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
- Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example (2016) — 654 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
- Which fMRI clustering gives good brain parcellations? (2014) — 347 citations
- Machine learning for neuroimaging with scikit-learn (2014) — 2,159 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
- Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses (2007) — 581 citations
- Inverse retinotopy: Inferring the visual content of images from brain activation patterns (2006) — 377 citations