Arild Nøkland

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 2 papers; trusts 300 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #3,509 of 35,345 by trust here, #16,471 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Optimization & Training — 20 citations into it; #427 of 2,601 by trust here, #1,488 by citations
• Model Architectures — 16 citations into it; #1,029 of 2,278 by trust here, #2,037 by citations
• Cognitive Neuroscience — 14 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Electrical and Electronic Engineering — 12 citations into it

Whom Arild Nøkland trusts overall

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

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

Who trusts Arild Nøkland overall

1. Eugene Belilovsky
2. Tsendsuren Munkhdalai
3. Lucas Caccia
4. Hesham Mostafa
5. Wojciech M. Czarnecki
6. Max Jaderberg
7. Xingdi Yuan
8. Sergey Bartunov
9. Sindy Löwe
10. Guillaume Bellec

Papers from Arild Nøkland's perspective

Ordered by how strongly the researchers Arild Nøkland 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Direct Feedback Alignment Provides Learning in Deep Neural Networks (2016) — 574 citations
• Training Neural Networks with Local Error Signals (2019) — 273 citations