Brent Harrison

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 12 papers; trusts 101 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #9,806 of 35,345 by trust here, #6,378 by citations
• Natural Language Processing — 68 citations into it; #2,005 of 5,688 by trust here, #1,518 by citations
• Reinforcement Learning — 65 citations into it; #1,636 of 2,242 by trust here, #1,118 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #20,123 of 21,575 by trust here, #12,419 by citations
• Model Architectures — 16 citations into it
• Safety, Robustness & Interpretability — 15 citations into it; #1,674 of 2,251 by trust here, #1,673 by citations
• Control and Systems Engineering — 13 citations into it
• Cognitive Neuroscience — 9 citations into it

Whom Brent Harrison trusts overall

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

1. Richard S. Sutton
2. Ilya Sutskever
3. Mark O. Riedl
4. Christopher J. C. H. Watkins
5. George A. Miller
6. Andrew Y. Ng
7. Sepp Hochreiter
8. Geoffrey E. Hinton
9. Christopher D. Manning
10. Yoshua Bengio

Who trusts Brent Harrison overall

1. Ramitha Sundar
2. Upol Ehsan
3. Sarah Wiegreffe
4. Angela Fan
5. Mark O. Riedl
6. Ana Marasovic
7. Jesse Mu
8. Eric
9. Michael Lewis
10. Tom Everitt

Papers from Brent Harrison's perspective

Ordered by how strongly the researchers Brent Harrison 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. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds (2017) — 39 citations
• Learning From Explanations Using Sentiment and Advice in RL (2017) — 76 citations
• Event Representations for Automated Story Generation with Deep Neural Nets (2017) — 231 citations
• Controllable Neural Story Plot Generation via Reward Shaping (2018) — 99 citations
• Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks (2021) — 39 citations
• Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations (2017) — 222 citations
• Automated rationale generation: a technique for explainable AI and its effects on human perceptions (2019) — 283 citations
• Learning From Stories: Using Crowdsourced Narratives to Train Virtual Agents (2021) — 11 citations
• Guiding Reinforcement Learning Exploration Using Natural Language (2017) — 29 citations
• Controllable Neural Story Plot Generation via Reinforcement Learning (2018) — 41 citations
• Controllable Neural Story Generation via Reinforcement Learning (2018) — 13 citations
• Using Stories to Teach Human Values to Artificial Agents (2016) — 91 citations