Zeb Kurth-Nelson

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 10 papers; trusts 62 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 161 citations into it; #10,756 of 35,345 by trust here, #3,412 by citations
• Cognitive Neuroscience — 155 citations into it; #2,537 of 10,852 by trust here, #536 by citations
• Reinforcement Learning — 103 citations into it; #939 of 2,242 by trust here, #326 by citations
• Cellular and Molecular Neuroscience — 64 citations into it
• Computer Vision and Pattern Recognition — 63 citations into it
• Neuroscience — 33 citations into it
• Meta & Continual Learning — 20 citations into it
• Management Science and Operations Research — 18 citations into it

Whom Zeb Kurth-Nelson trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Volodymyr Mnih
5. Alex Graves
6. Yoshua Bengio
7. Sepp Hochreiter
8. David Silver
9. Andrew Barto
10. Peter S. Dayan

Who trusts Zeb Kurth-Nelson overall

1. Chelsea Finn
2. Yuxi Li
3. Sergey Levine
4. Jane X. Wang
5. Abhishek Gupta
6. Samuel Ritter
7. Pieter Abbeel
8. Jeff Clune
9. Matthew Botvinick
10. Luckeciano Carvalho Melo

Papers from Zeb Kurth-Nelson's perspective

Ordered by how strongly the researchers Zeb Kurth-Nelson 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning to reinforcement learn (2016) — 1,097 citations
• Causal Reasoning from Meta-reinforcement Learning (2019) — 138 citations
• Been There, Done That: Meta-Learning with Episodic Recall (2018) — 95 citations
• Meta-learning of Sequential Strategies (2019) — 120 citations
• What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior. (2018) — 941 citations
• A distributional code for value in dopamine-based reinforcement learning (2020) — 418 citations
• Human Replay Spontaneously Reorganizes Experience (2019) — 402 citations
• Reinforcement Learning, Fast and Slow. (2019) — 659 citations
• Prefrontal cortex as a meta-reinforcement learning system (2018) — 627 citations
• Model-Based Reasoning in Humans Becomes Automatic with Training (2015) — 80 citations