Andrew Y. Ng
Impact in
- Artificial Intelligence top 0.01%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Sentiment Analysis and Opinion Mining
- Text and Document Classification Technologies
- Computer Vision and Pattern Recognition top 0.01%
- Advanced Image and Video Retrieval Techniques
- Face and Expression Recognition
Papers in
-
- Topic Modeling 30
- Machine Learning and Algorithms 25
- Natural Language Processing Techniques 24
- Reinforcement Learning in Robotics 19
- Neural Networks and Applications 14
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- Advanced Image and Video Retrieval Techniques 23
- Advanced Vision and Imaging 15
- Co-authors
- Michael I. Jordan (12 shared papers)David M. Blei (2 shared papers)Honglak Lee (12 shared papers)Christopher D. Manning (14 shared papers)Richard Socher (10 shared papers)Yair Weiss (1 shared paper)Pieter Abbeel (15 shared papers)Adam Coates (17 shared papers)
- Journals
- The International Journal of Robotics Research (6 papers)Patterns (2 papers)Journal of Machine Learning Research (2 papers)JAMA Network Open (2 papers)Communications of the ACM (2 papers)
- Partner nations
- United StatesFranceIsrael
In The Last Decade
Andrew Y. Ng
207 papers receiving 66.1k citations
Andrew Y. Ng's Hit Papers
Peers
Comparison fields: 5 of 232
- Artificial Intelligence 40.2k
- Computer Vision and Pattern Recognition 21.2k
- Signal Processing 5.4k
- Health Informatics 624
- Computational Mathematics 257
Countries citing papers authored by Andrew Y. Ng
This map shows the geographic impact of Andrew Y. Ng'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 Andrew Y. Ng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Y. Ng more than expected).
Fields of papers citing papers by Andrew Y. Ng
This network shows the impact of papers produced by Andrew Y. Ng. 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 Andrew Y. Ng. The network helps show where Andrew Y. Ng may publish in the future.
Co-authors
The 25 scholars most cited alongside Andrew Y. Ng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 209 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Latent dirichlet allocation Hit paper breakdown → | 2003 | 18136 |
| 2 | On Spectral Clustering: Analysis and an algorithm Hit paper breakdown → | 2001 | 5108 |
| 3 | Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Hit paper breakdown → | 2013 | 3712 |
| 4 | Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network Hit paper breakdown → | 2018 | 1886 |
| 5 | Learning Word Vectors for Sentiment Analysis Hit paper breakdown → | 2011 | 1826 |
| 6 | Distance Metric Learning with Application to Clustering with Side-Information Hit paper breakdown → | 2002 | 1735 |
| 7 | Large Scale Distributed Deep Networks Hit paper breakdown → | 2012 | 1718 |
| 8 | Apprenticeship learning via inverse reinforcement learning Hit paper breakdown → | 2004 | 1572 |
| 9 | Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations Hit paper breakdown → | 2009 | 1535 |
| 10 | Multimodal Deep Learning Hit paper breakdown → | 2011 | 1432 |
| 11 | An analysis of single-layer networks in unsupervised feature learning Hit paper breakdown → | 2011 | 1395 |
| 12 | Cheap and fast---but is it good? Hit paper breakdown → | 2008 | 1333 |
| 13 | CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison Hit paper breakdown → | 2019 | 1307 |
| 14 | On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes Hit paper breakdown → | 2001 | 1266 |
| 15 | Self-taught learning Hit paper breakdown → | 2007 | 1015 |
| 16 | Feature selection, L1 vs. L2 regularization, and rotational invariance Hit paper breakdown → | 2004 | 972 |
| 17 | Reasoning With Neural Tensor Networks for Knowledge Base Completion Hit paper breakdown → | 2013 | 922 |
| 18 | Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping Hit paper breakdown → | 1999 | 852 |
| 19 | Semantic Compositionality through Recursive Matrix-Vector Spaces Hit paper breakdown → | 2012 | 773 |
| 20 | Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions Hit paper breakdown → | 2011 | 747 |
About Andrew Y. Ng
Andrew Y. Ng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Aerospace Engineering, Control and Systems Engineering and Signal Processing, having authored 209 papers that have together received 70.6k indexed citations. Recurring topics across this work include Topic Modeling (30 papers), Machine Learning and Algorithms (25 papers), Natural Language Processing Techniques (24 papers), Advanced Image and Video Retrieval Techniques (23 papers), Reinforcement Learning in Robotics (19 papers), Robotics and Sensor-Based Localization (19 papers), Advanced Vision and Imaging (15 papers) and Neural Networks and Applications (14 papers). The work is most often cited by research in Artificial Intelligence (40.2k citations), Computer Vision and Pattern Recognition (21.2k citations), Signal Processing (5.4k citations), Health Informatics (624 citations) and Computational Mathematics (257 citations). Andrew Y. Ng has collaborated with scholars based in United States, France and Israel. Frequent co-authors include Michael I. Jordan, David M. Blei, Honglak Lee, Christopher D. Manning, Richard Socher, Yair Weiss, Pieter Abbeel, Adam Coates, Christopher Potts and Ashutosh Saxena. Their work appears in journals such as The International Journal of Robotics Research, Patterns, Journal of Machine Learning Research, JAMA Network Open and Communications of the ACM.
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.