Julien Cohen-Adad

Computer Vision and Pattern Recognition · 2012–2022

A researcher in the Science corpus: 9 papers; trusts 386 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 187 citations into it
• Artificial Intelligence — 124 citations into it; #30,950 of 35,345 by trust here, #18,186 by citations
• Radiology, Nuclear Medicine and Imaging — 110 citations into it; #2,486 of 3,265 by trust here, #934 by citations
• Model Architectures — 29 citations into it
• Optimization & Training — 23 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Efficiency & Compression — 10 citations into it

Whom Julien Cohen-Adad trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yann LeCun
6. Yoshua Bengio
7. Ross B. Girshick
8. Ilya Sutskever
9. Karen Simonyan
10. Christian Szegedy

Who trusts Julien Cohen-Adad overall

1. David A. Feinberg

Papers from Julien Cohen-Adad's perspective

Ordered by how strongly the researchers Julien Cohen-Adad 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation (2022) — 389 citations
• Deep semantic segmentation of natural and medical images: a review (2019) — 826 citations
• Unsupervised domain adaptation for medical imaging segmentation with self-ensembling (2018) — 249 citations
• SCT: Spinal Cord Toolbox, an open‐source software for processing spinal cord MRI data (2017) — 637 citations
• In vivo histology of the myelin g-ratio with magnetic resonance imaging (2015) — 308 citations
• The current state-of-the-art of spinal cord imaging: Methods (2013) — 332 citations
• Pushing the limits of in vivo diffusion MRI for the Human Connectome Project (2013) — 568 citations
• The Human Connectome Project and Beyond: Initial Applications of 300 mT/m Gradients (2013) — 372 citations
• Improving diffusion MRI using simultaneous multi-slice echo planar imaging (2012) — 376 citations