Michaël Defferrard

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 213 researchers and is trusted by 265 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 62 citations into it
• Artificial Intelligence — 59 citations into it; #1,276 of 35,345 by trust here, #1,571 by citations
• Graph Learning — 38 citations into it; #20 of 693 by trust here, #33 by citations
• Model Architectures — 30 citations into it
• Computational Mechanics — 16 citations into it
• Signal Processing — 16 citations into it
• Statistical and Nonlinear Physics — 16 citations into it; #80 of 10,127 by trust here, #79 by citations
• Representation & Self-Supervised Learning — 14 citations into it

Whom Michaël Defferrard trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ilya Sutskever
7. Alex Graves
8. Thorsten Joachims
9. Tomas Mikolov
10. Kaiming He

Who trusts Michaël Defferrard overall

1. Michael M. Bronstein
2. Aleksandar Bojchevski
3. Petar Velickovic
4. Federico Monti
5. Yao Ma
6. L. Zampieri
7. Stephan Günnemann
8. Max Welling
9. Xavier Bresson
10. Thomas Kipf

Papers from Michaël Defferrard's perspective

Ordered by how strongly the researchers Michaël Defferrard 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (2016) — 8,688 citations
• Structured Sequence Modeling with Graph Convolutional Recurrent Networks (2016) — 984 citations
• DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications (2018) — 197 citations
• DeepSphere: a graph-based spherical CNN (2020) — 103 citations
• FMA: A Dataset for Music Analysis (2016) — 637 citations