Adam Henry Marblestone

Cognitive Neuroscience · 2009–2018

A researcher in the Science corpus: 6 papers; trusts 212 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 118 citations into it; #6,085 of 10,852 by trust here, #2,905 by citations
• Artificial Intelligence — 108 citations into it
• Computer Vision and Pattern Recognition — 64 citations into it
• Cellular and Molecular Neuroscience — 48 citations into it; #1,882 of 2,998 by trust here, #2,655 by citations
• Reinforcement Learning — 38 citations into it
• Electrical and Electronic Engineering — 34 citations into it
• Neuroscience — 34 citations into it; #304 of 487 by trust here, #189 by citations
• Model Architectures — 31 citations into it

Whom Adam Henry Marblestone trusts overall

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

1. Geoffrey E. Hinton
2. Paul J. Werbos
3. Yoshua Bengio
4. Alex Graves
5. Ilya Sutskever
6. Alex Krizhevsky
7. Yann LeCun
8. J. Urgen Schmidhuber
9. Diederik P. Kingma
10. Andrew Barto

Who trusts Adam Henry Marblestone overall

1. Sara Hooker
2. Eiji Watanabe
3. Arvid Lundervold
4. João Sacramento
5. Gary F. Marcus
6. Akiyoshi Kitaoka
7. Radoslaw Martin Cichy
8. Matthew R. Jones
9. Nikolaus Kriegeskorte
10. Friedemann Zenke

Papers from Adam Henry Marblestone's perspective

Ordered by how strongly the researchers Adam Henry Marblestone 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Highly multiplexed subcellular RNA sequencing in situ (2014) — 944 citations
• Toward an Integration of Deep Learning and Neuroscience (2016) — 683 citations
• Malthusian Reinforcement Learning (2018) — 43 citations
• Rapid prototyping of 3D DNA-origami shapes with caDNAno (2009) — 1,160 citations
• Grand Challenges for Global Brain Sciences (2016)
• The atoms of neural computation (2014) — 79 citations