John Quan

12.0k citations

8 papers · 4.4k indexed · 2 hit papers · h-index 6

Impact in

Artificial Intelligence top 0.2%
- Domain Adaptation and Few-Shot Learning
- Reinforcement Learning in Robotics
- Machine Learning and ELM
- Topic Modeling
- Anomaly Detection Techniques and Applications
Computer Vision and Pattern Recognition top 0.5%
- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Human Pose and Action Recognition

Papers in

Artificial Intelligence 7
- Reinforcement Learning in Robotics 5
- Neural Networks and Applications 1
Safety, Risk, Reliability and Quality 1
- Evaluation and Optimization Models 1

Co-authors: Neil C. Rabinowitz James Kirkpatrick Dharshan Kumaran Raia Hadsell Guillaume Desjardins Agnieszka Grabska‐Barwińska Demis Hassabis Tiago Ramalho
Journals: Proceedings of the National Academy of Sciences (1 paper)IT Professional (1 paper)Measurement Science and Technology (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (2 papers)
Partner nations: United States United Kingdom Israel

In The Last Decade

John Quan

7 papers receiving 4.2k citations

Hit Papers

align trajectories log scale

Deep Q-learning From Demonstrations 2018 · 485 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2018 Proceedings of the AAAI Conference on Artificial Intelligence
2017 Proceedings of the National Academy of Sciences

Peers

Countries citing papers authored by John Quan

Since Specialization

Citations

This map shows the geographic impact of John Quan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John Quan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Quan more than expected).

Fields of papers citing papers by John Quan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Quan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John Quan. The network helps show where John Quan may publish in the future.

Co-authorship network

The 25 scholars most cited alongside John Quan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John Quan Line = papers co-authored together John Quan links everyone, so they are left out of the graph.

All Works

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8 of 8 papers shown

#	Work
1	Coal and gas outburst risk level prediction model based on Bayesian theory and its application Measurement Science and Technology ·刘宜伟, Dazhao Song, Fathin Najla, Liming Qiu, Rolando Pérez Sánchez, Lin Liu, John Quan, Qiang Liu	2025	1
2	Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement arXiv (Cornell University) ·André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Žídek, Rémi Munos	2019	19
3	Distributed Prioritized Experience Replay International Conference on Learning Representations ·Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver	2018	56
4	Recurrent Experience Replay in Distributed Reinforcement Learning. International Conference on Learning Representations ·Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney	2018	94
5	Deep Q-learning From Demonstrations Proceedings of the AAAI Conference on Artificial Intelligence ·Todd Hester, Jawaz Alam, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Lt Col Khawaja Khalid Shoaib, Ian Osband, Gabriel Dulac-Arnold, John Agapiou, Joel Z. Leibo, Audrūnas Gruslys Hit paper breakdown →	2018	485
6	Overcoming catastrophic forgetting in neural networks Proceedings of the National Academy of Sciences ·James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska‐Barwińska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell Hit paper breakdown →	2017	3594
7	Deep Q-learning from Demonstrations arXiv (Cornell University) ·Todd Hester, Jawaz Alam, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Lt Col Khawaja Khalid Shoaib, Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrūnas Gruslys	2017	123
8	A Mutualistic Security Service Model: Supporting Large-Scale Virtualized Environments IT Professional ·John Quan, Kara Nance, Brian Hay	2011	2

About John Quan

John Quan is a scholar working on Artificial Intelligence, Safety, Risk, Reliability and Quality, Signal Processing, Statistical and Nonlinear Physics and Management Science and Operations Research, having authored 8 papers that have together received 4.4k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (5 papers), Sports Analytics and Performance (2 papers), Coal Properties and Utilization (1 paper), Neural dynamics and brain function (1 paper), Neural Networks and Applications (1 paper), Evaluation and Optimization Models (1 paper), Smart Grid Energy Management (1 paper) and Software Engineering Research (1 paper). The work is most often cited by research in Artificial Intelligence (3.2k citations), Computer Vision and Pattern Recognition (1.6k citations), Health Informatics (41 citations), Control and Systems Engineering (410 citations) and Signal Processing (175 citations). John Quan has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Neil C. Rabinowitz, James Kirkpatrick, Dharshan Kumaran, Raia Hadsell, Guillaume Desjardins, Agnieszka Grabska‐Barwińska, Demis Hassabis, Tiago Ramalho, Andrei A. Rusu and Claudia Clopath. Their work appears in journals such as Proceedings of the National Academy of Sciences, IT Professional, Measurement Science and Technology, Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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