Brendan McCane

Artificial Intelligence · 2001–2018

A researcher in the Science corpus: 3 papers; trusts 31 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #20,786 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 21 citations into it; #18,793 of 21,575 by trust here, #12,172 by citations
• Reinforcement Learning — 16 citations into it
• Cognitive Neuroscience — 12 citations into it
• Meta & Continual Learning — 10 citations into it; #940 of 1,041 by trust here, #782 by citations
• Neuroscience — 5 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Brendan McCane trusts overall

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

1. George Wolberg
2. Anthony V. Robins
3. Geoffrey E. Hinton
4. Richard S. Sutton
5. Alex Krizhevsky
6. Edward H. Adelson
7. Kaiming He
8. Yoshua Bengio
9. Hugo Caselles-Dupré
10. Ilya Sutskever

Who trusts Brendan McCane overall

1. Diana Benavides Prado
2. Md Atiqur Rahman Ahad
3. Matthias De Lange
4. Mustafa Burak Gurbuz
5. Philipp Krahenbuhl
6. Yun Sing Koh
7. Hagai Taitelbaum
8. Joo Kooi Tan
9. Glen Berseth

Papers from Brendan McCane's perspective

Ordered by how strongly the researchers Brendan McCane 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• On Benchmarking Optical Flow (2001) — 295 citations
• 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