Adam Coates

14.4k citations

27 papers · 4.4k indexed · 6 hit papers · h-index 17

Impact in

Computer Vision and Pattern Recognition top 0.2%
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Handwritten Text Recognition Techniques
- Image Retrieval and Classification Techniques
Artificial Intelligence top 0.5%
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Reinforcement Learning in Robotics

Papers in

Computer Vision and Pattern Recognition 15
- Advanced Image and Video Retrieval Techniques 8
- Advanced Neural Network Applications 4
- Robotic Path Planning Algorithms 4
Artificial Intelligence 13
- Reinforcement Learning in Robotics 4
- Domain Adaptation and Few-Shot Learning 4
- Speech Recognition and Synthesis 3

Co-authors: Andrew Y. Ng Honglak Lee David J. Wu Tao Wang Pieter Abbeel Quoc V. Le Jiquan Ngiam Bryan Catanzaro
Journals: The International Journal of Robotics Research (2 papers)Data in Brief (1 paper)Communications of the ACM (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)Neural Information Processing Systems (2 papers)
Partner nations: United States Austria United Kingdom

In The Last Decade

Adam Coates

27 papers receiving 4.2k citations

Hit Papers

6 papers align trajectories log scale

Deep learning with COTS HPC systems 2013 · 370 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.

2013 International Conference on Machine Learning
2012 End-to-End Text Recognition with Convolutional Neural Networks
2011 International Conference on Machine Learning
2011 International Conference on Machine Learning
2011 International Conference on Artificial Intelligence and Statistics
2010 The International Journal of Robotics Research

Peers

Countries citing papers authored by Adam Coates

Since Specialization

Citations

This map shows the geographic impact of Adam Coates'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 Adam Coates with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adam Coates more than expected).

Fields of papers citing papers by Adam Coates

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Adam Coates. 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 Adam Coates. The network helps show where Adam Coates may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Adam Coates, 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 Adam Coates Line = papers co-authored together Adam Coates links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work
1	High-resolution dataset of manual claustrum segmentation Data in Brief ·Adam Coates, Natalia Zaretskaya	2024	3
2	High-resolution 7T fMRI reveals the visual zone of the human claustrum Imaging Neuroscience ·Adam Coates, Wang Junbing, Christian Windischberger, Anja Ischebeck, Natalia Zaretskaya	2024	2
3	Deep Generative and Discriminative Domain Adaptation Han Zhao, Junjie Hu, Zhenyao Zhu, Adam Coates, Geoff Gordon	2019	1
4	Deep Voice: Real-time Neural Text-to-Speech International Conference on Machine Learning ·Sercan Ö. Arık, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, J. J. Miller, Andrew Y. Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi	2017	39
5	Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting Sercan Ö. Arık, Chia-Nan Chen, Rewon Child, Joel Hestness, Andrew Gibiansky, F Castro, Ryan Prenger, Adam Coates	2017	106
6	Persistent RNNs: stashing recurrent weights on-chip International Conference on Machine Learning ·Gregory Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh	2016	36
7	Deep learning with COTS HPC systems International Conference on Machine Learning ·Adam Coates, Brody Huval, Tao Wang, David J. Wu, Bryan Catanzaro, Andrew Ng Hit paper breakdown →	2013	370
8	Deep Learning for Machine Vision Adam Coates	2013	2
9	Emergence of Object-Selective Features in Unsupervised Feature Learning Neural Information Processing Systems ·Adam Coates, Andrej Karpathy, Andrew Y. Ng	2012	25
10	End-to-End Text Recognition with Convolutional Neural Networks Tao Wang, David J. Wu, Adam Coates, Andrew Y. Ng Hit paper breakdown →	2012	540
11	The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization International Conference on Machine Learning ·Adam Coates, Andrew Y. Ng Hit paper breakdown →	2011	314
12	An analysis of single-layer networks in unsupervised feature learning International Conference on Artificial Intelligence and Statistics ·Adam Coates, Andrew Y. Ng, Honglak Lee Hit paper breakdown →	2011	1385
13	On optimization methods for deep learning International Conference on Machine Learning ·Jiquan Ngiam, Adam Coates, Yoshi Strik, Richard R. Gomez, Quoc V. Le, Andrew Y. Ng Hit paper breakdown →	2011	571
14	Autonomous sign reading for semantic mapping Carl Case, Bipin Suresh, Adam Coates, Andrew Y. Ng	2011	30
15	Multi-camera object detection for robotics Adam Coates, Andrew Y. Ng	2010	34
16	Scalable learning for object detection with GPU hardware Adam Coates, Shun Chiao Chang, Quoc V. Le, Andrew Y. Ng	2009	32
17	Apprenticeship learning for helicopter control Communications of the ACM ·Adam Coates, Pieter Abbeel, Andrew Y. Ng	2009	67
18	Learning for control from multiple demonstrations Adam Coates, Pieter Abbeel, Andrew Y. Ng	2008	154
19	Time Complexity of Sensor-Based Vehicle Routing Pieter Abbeel, Adam Coates, Michael Montemerlo, Andrew Y. Ng, Sebastian Thrun	2005	1
20	The Beagle 2 Stereo Camera System: Scientific Objectives and Design Characteristics EGS - AGU - EUG Joint Assembly ·Alison Griffiths, Adam Coates, Jean‐Luc Josset, Gerhard Paar, Margaret Sims	2003	2

About Adam Coates

Adam Coates is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Aerospace Engineering and Geology, having authored 27 papers that have together received 4.4k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (8 papers), Robotics and Sensor-Based Localization (5 papers), Advanced Neural Network Applications (4 papers), Reinforcement Learning in Robotics (4 papers), Robotic Path Planning Algorithms (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Music and Audio Processing (3 papers) and Speech Recognition and Synthesis (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.5k citations), Artificial Intelligence (2.0k citations), Media Technology (507 citations), Computational Mathematics (28 citations) and Signal Processing (404 citations). Adam Coates has collaborated with scholars based in United States, Austria and United Kingdom. Frequent co-authors include Andrew Y. Ng, Honglak Lee, David J. Wu, Tao Wang, Pieter Abbeel, Quoc V. Le, Jiquan Ngiam, Bryan Catanzaro, Andrew Ng and Brody Huval. Their work appears in journals such as The International Journal of Robotics Research, Data in Brief, Communications of the ACM, International Conference on Artificial Intelligence and Statistics and Neural Information Processing Systems.

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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