Guillaume Bellec

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 33 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #22,237 of 35,345 by trust here, #14,718 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #14,906 of 21,575 by trust here, #11,498 by citations
• Cognitive Neuroscience — 27 citations into it; #5,717 of 10,852 by trust here, #4,443 by citations
• Efficiency & Compression — 23 citations into it; #843 of 1,264 by trust here, #899 by citations
• Model Architectures — 18 citations into it
• Cellular and Molecular Neuroscience — 13 citations into it
• Signal Processing — 12 citations into it
• Optimization & Training — 11 citations into it

Whom Guillaume Bellec trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Graves
4. Diederik P. Kingma
5. Ronald J. Williams
6. Yann LeCun
7. Alex Krizhevsky
8. Max Welling
9. J. Urgen Schmidhuber
10. Robert Tibshirani

Who trusts Guillaume Bellec overall

1. Hesham Mostafa
2. Friedemann Zenke
3. Emre O. Neftci
4. Tim Dettmers
5. Nitin Rathi
6. Jason Kamran Eshraghian
7. Zengyi Li
8. Shiwei Liu
9. Gopalakrishnan Srinivasan
10. Jonathan Frankle

Papers from Guillaume Bellec's perspective

Ordered by how strongly the researchers Guillaume Bellec 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. Going deeper with convolutions

Their papers

• Long short-term memory and Learning-to-learn in networks of spiking neurons (2018) — 601 citations
• Deep Rewiring: Training very sparse deep networks (2017) — 311 citations
• A solution to the learning dilemma for recurrent networks of spiking neurons (2019) — 631 citations
• Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype (2018) — 74 citations