Colin J. Akerman

Cognitive Neuroscience · 2014–2020

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 52 citations into it; #1,578 of 10,852 by trust here, #2,252 by citations
• Artificial Intelligence — 34 citations into it; #7,773 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Cellular and Molecular Neuroscience — 22 citations into it
• Optimization & Training — 20 citations into it; #2,315 of 2,601 by trust here, #1,585 by citations
• Electrical and Electronic Engineering — 17 citations into it; #1,598 of 16,539 by trust here, #4,543 by citations
• Reinforcement Learning — 14 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it

Whom Colin J. Akerman trusts overall

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

1. Geoffrey E. Hinton
2. Ronald J. Williams
3. Volodymyr Mnih
4. David E. Rumelhart
5. Yoshua Bengio
6. Paul J. Werbos
7. Randall C. O’Reilly
8. David H. Ackley
9. Yann LeCun
10. H. Sebastian Seung

Who trusts Colin J. Akerman overall

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Hesham Mostafa
4. Friedemann Zenke
5. Emre O. Neftci
6. Tsendsuren Munkhdalai
7. Guangyu Robert Yang
8. James C. R. Whittington
9. Jordan Guerguiev

Papers from Colin J. Akerman's perspective

Ordered by how strongly the researchers Colin J. Akerman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Gradient-based learning applied to document recognition
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. A Fast Learning Algorithm for Deep Belief Nets
7. This PDF file includes: Materials and Methods
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Reinforcement Learning: An Introduction

Their papers

• Random synaptic feedback weights support error backpropagation for deep learning (2016) — 953 citations
• Random feedback weights support learning in deep neural networks (2014) — 186 citations
• Backpropagation and the brain (2020) — 962 citations