Christian Desrosiers

Computer Vision and Pattern Recognition · 2011–2021

A researcher in the Science corpus: 5 papers; trusts 220 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #20,519 of 21,575 by trust here, #11,286 by citations
• Artificial Intelligence — 51 citations into it
• Information Systems — 24 citations into it; #2,697 of 8,771 by trust here, #5,942 by citations
• Radiology, Nuclear Medicine and Imaging — 14 citations into it; #3,088 of 3,265 by trust here, #2,321 by citations
• Management Science and Operations Research — 9 citations into it; #3,996 of 11,946 by trust here, #7,262 by citations
• Recommender Systems — 7 citations into it
• Signal Processing — 7 citations into it
• Cognitive Neuroscience — 5 citations into it

Whom Christian Desrosiers trusts overall

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

1. George Karypis
2. Xia Ning
3. Daniel Billsus
4. Yehuda Koren
5. J. Konstan
6. Michael J. Pazzani
7. Thomas Hofmann
8. Paul Resnick
9. Jonathan L. Herlocker
10. Badrul Munir Sarwar

Who trusts Christian Desrosiers overall

1. Charu C. Aggarwal
2. Marius Kaminskas
3. Weike Pan
4. Arnaud Berlioz
5. Derek G. Bridge
6. Fabio Aiolli
7. Qiang Yang
8. Jose Dolz
9. Arik Friedman
10. Satya Prakash Singh

Papers from Christian Desrosiers's perspective

Ordered by how strongly the researchers Christian Desrosiers 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. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Comprehensive Survey of Neighborhood-based Recommendation Methods (2011) — 988 citations
• Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature (2021) — 348 citations
• HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation (2018) — 515 citations
• Boundary loss for highly unbalanced segmentation (2018) — 736 citations
• 3D fully convolutional networks for subcortical segmentation in MRI: A large‐scale study (2016) — 388 citations