Jordan Guerguiev

Cognitive Neuroscience · 2016–2020

A researcher in the Science corpus: 2 papers; trusts 40 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 57 citations into it; #4,018 of 10,852 by trust here, #6,180 by citations
• Cellular and Molecular Neuroscience — 33 citations into it
• Electrical and Electronic Engineering — 24 citations into it; #4,992 of 16,539 by trust here, #9,631 by citations
• Artificial Intelligence — 23 citations into it; #16,681 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Neuroscience — 13 citations into it
• Optimization & Training — 12 citations into it

Whom Jordan Guerguiev trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ronald J. Williams
4. Ilya Sutskever
5. Yann LeCun
6. Alex Krizhevsky
7. David E. Rumelhart
8. Kaiming He
9. Timothy P. Lillicrap
10. Ruslan Salakhutdinov

Who trusts Jordan Guerguiev overall

1. Guangyu Robert Yang
2. James C. R. Whittington
3. Emre O. Neftci
4. Wulfram Gerstner
5. Rafal Bogacz
6. Xiao-Jing Wang
7. Geoffrey E. Hinton
8. Friedemann Zenke
9. Hesham Mostafa
10. João Sacramento

Papers from Jordan Guerguiev's perspective

Ordered by how strongly the researchers Jordan Guerguiev 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. Long Short-Term Memory
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. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Towards deep learning with segregated dendrites (2016) — 423 citations
• Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits (2020) — 331 citations