Patrick Mannion

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 69 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 200 citations into it
• Reinforcement Learning — 166 citations into it
• Automotive Engineering — 60 citations into it
• Computer Vision and Pattern Recognition — 59 citations into it
• Control and Systems Engineering — 45 citations into it
• Computational Theory and Mathematics — 42 citations into it
• Management Science and Operations Research — 31 citations into it
• Robotics & Control — 28 citations into it

Whom Patrick Mannion trusts overall

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

1. Richard S. Sutton
2. Ben J. A. Kröse
3. Lucian Busoniu
4. Sam Devlin
5. Andrew Barto
6. Andrew Y. Ng
7. Malabika Basu
8. Geoffrey E. Hinton
9. Christopher J. C. H. Watkins
10. Pieter Abbeel

Who trusts Patrick Mannion overall

1. B. R. Kiran
2. Roxana Rădulescu

Papers from Patrick Mannion's perspective

Ordered by how strongly the researchers Patrick Mannion 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. Reinforcement Learning: An Introduction
8. ImageNet: A large-scale hierarchical image database
9. Human-level control through deep reinforcement learning
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Reinforcement Learning for Autonomous Driving: A Survey (2020) — 2,407 citations
• A Theoretical and Empirical Analysis of Reward Transformations in Multi-Objective Stochastic Games (2017) — 11 citations
• Multi-objective multi-agent decision making: a utility-based analysis and survey (2019) — 188 citations
• Policy invariance under reward transformations for multi-objective reinforcement learning (2017) — 57 citations
• A practical guide to multi-objective reinforcement learning and planning (2021) — 587 citations
• Exploring applications of deep reinforcement learning for real-world autonomous driving systems (2019) — 81 citations