Craig Atkinson

Artificial Intelligence · with McCane · 2018

A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #20,787 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #18,794 of 21,575 by trust here, #14,478 by citations
• Reinforcement Learning — 16 citations into it
• Cognitive Neuroscience — 12 citations into it
• Meta & Continual Learning — 10 citations into it; #941 of 1,041 by trust here, #782 by citations
• Statistical and Nonlinear Physics — 5 citations into it

Whom Craig Atkinson trusts overall

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

1. Anthony V. Robins
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Richard S. Sutton
5. Kaiming He
6. Hugo Caselles-Dupré
7. Yoshua Bengio
8. Ilya Sutskever
9. Bernard Ans
10. Diederik P. Kingma

Who trusts Craig Atkinson overall

1. Diana Benavides Prado
2. Matthias De Lange
3. Mustafa Burak Gurbuz
4. Yun Sing Koh
5. Hagai Taitelbaum
6. Glen Berseth
7. Rahaf Aljundi
8. Patricia J. Riddle
9. C. Dovrolis
10. Myle Ott

Papers from Craig Atkinson's perspective

Ordered by how strongly the researchers Craig Atkinson 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks (2018) — 78 citations
• Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting (2018) — 121 citations