Murray Campbell

Artificial Intelligence · 1995–2020

A researcher in the Science corpus: 17 papers; trusts 742 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 285 citations into it; #7,915 of 35,345 by trust here, #3,663 by citations
• Computer Vision and Pattern Recognition — 209 citations into it; #12,711 of 21,575 by trust here, #4,525 by citations
• Reinforcement Learning — 119 citations into it; #793 of 2,242 by trust here, #667 by citations
• Natural Language Processing — 75 citations into it
• Representation & Self-Supervised Learning — 57 citations into it
• Aerospace Engineering — 51 citations into it; #4,333 of 6,421 by trust here, #1,237 by citations
• Optimization & Training — 32 citations into it
• Evaluation & Benchmarks — 29 citations into it

Whom Murray Campbell trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ross B. Girshick
7. Yann LeCun
8. Ilya Sutskever
9. Richard S. Sutton
10. Judea Pearl

Who trusts Murray Campbell overall

1. Noam Brown
2. David Silver
3. Michael L. Littman
4. Hengyuan Hu
5. Johannes Heinrich
6. Tuomas Sandholm
7. Rasheed Hussain
8. Nadav Bhonker
9. Jakob N. Foerster
10. Mehdi Samadi

Papers from Murray Campbell's perspective

Ordered by how strongly the researchers Murray Campbell 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving (2018) — 1,180 citations
• Deep Blue (2002) — 1,460 citations
• Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering (2017) — 166 citations
• Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving (2019) — 446 citations
• Eigenoption Discovery through the Deep Successor Representation (2017) — 172 citations
• Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering (2019) — 11 citations
• All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles (2016) — 34 citations
• Learning Hierarchical Teaching Policies for Cooperative Agents (2019) — 33 citations
• Hybrid Reinforcement Learning with Expert State Sequences (2019) — 36 citations
• Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning (2020) — 35 citations
• Learning to Teach in Cooperative Multiagent Reinforcement Learning (2018) — 145 citations
• Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration (2018) — 25 citations
• Autonomous driving in urban environments: approaches, lessons and challenges (2010) — 366 citations
• Revisiting Meta-Learning as Supervised Learning (2020) — 24 citations
• Continual learning with direction-constrained optimization (2020)
• TED: Teaching AI to Explain its Decisions (2018) — 115 citations
• Deep Blue system overview (1995)