George E. Dahl
Impact in
- Signal Processing top 0.02%
- Speech and Audio Processing
- Music and Audio Processing
- Artificial Intelligence top 0.02%
- Speech Recognition and Synthesis
- Neural Networks and Applications
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
Papers in
-
- Speech Recognition and Synthesis 12
- Topic Modeling 4
- Neural Networks and Applications 3
- Natural Language Processing Techniques 3
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- Music and Audio Processing 9
- Speech and Audio Processing 9
- Co-authors
- Geoffrey E. Hinton (7 shared papers)Abdelrahman Mohamed (7 shared papers)Tara N. Sainath (6 shared papers)Dong Yu (6 shared papers)Li Deng (6 shared papers)Brian Kingsbury (4 shared papers)Navdeep Jaitly (3 shared papers)Vincent Vanhoucke (3 shared papers)
- Journals
- IEEE Signal Processing Magazine (2 papers)IEEE Transactions on Audio Speech and Language Processing (2 papers)Nature Communications (1 paper)Archives of Pathology & Laboratory Medicine (1 paper)Journal of Chemical Information and Modeling (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
George E. Dahl
25 papers receiving 16.6k citations
George E. Dahl's Hit Papers
Peers
Comparison fields: 5 of 215
- Signal Processing 5.5k
- Artificial Intelligence 9.8k
- Computer Vision and Pattern Recognition 3.8k
- Health Informatics 104
- Computational Theory and Mathematics 1.1k
Countries citing papers authored by George E. Dahl
This map shows the geographic impact of George E. Dahl'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 George E. Dahl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites George E. Dahl more than expected).
Fields of papers citing papers by George E. Dahl
This network shows the impact of papers produced by George E. Dahl. 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 George E. Dahl. The network helps show where George E. Dahl may publish in the future.
Co-authors
The 25 scholars most cited alongside George E. Dahl, 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 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups Hit paper breakdown → | 2012 | 6687 |
| 2 | Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition Hit paper breakdown → | 2011 | 2084 |
| 3 | On the importance of initialization and momentum in deep learning Hit paper breakdown → | 2013 | 1906 |
| 4 | Acoustic Modeling Using Deep Belief Networks Hit paper breakdown → | 2011 | 1242 |
| 5 | Deep Neural Networks for Acoustic Modeling in Speech Recognition Hit paper breakdown → | 2012 | 1170 |
| 6 | Improving deep neural networks for LVCSR using rectified linear units and dropout Hit paper breakdown → | 2013 | 936 |
| 7 | Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships Hit paper breakdown → | 2015 | 798 |
| 8 | Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) Hit paper breakdown → | 2010 | 561 |
| 9 | Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error Hit paper breakdown → | 2017 | 466 |
| 10 | Deep Convolutional Neural Networks for Large-scale Speech Tasks Hit paper breakdown → | 2014 | 367 |
| 11 | Large-scale malware classification using random projections and neural networks Hit paper breakdown → | 2013 | 271 |
| 12 | 2018 | 253 | |
| 13 | Deep Belief Networks using discriminative features for phone recognition Hit paper breakdown → | 2011 | 196 |
| 14 | Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine | 2010 | 185 |
| 15 | Los peces del norte de Colombia | 1971 | 139 |
| 16 | Roles of Pre-Training and Fine-Tuning in Context-Dependent DBN-HMMs for Real-World Speech Recognition | 2010 | 133 |
| 17 | 2011 | 114 | |
| 18 | 2021 | 111 | |
| 19 | 2014 | 31 | |
| 20 | 2012 | 30 |
About George E. Dahl
George E. Dahl is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Materials Chemistry, having authored 25 papers that have together received 17.7k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (12 papers), Music and Audio Processing (9 papers), Speech and Audio Processing (9 papers), Topic Modeling (4 papers), Neural Networks and Applications (3 papers), Computational Drug Discovery Methods (3 papers), Natural Language Processing Techniques (3 papers) and Machine Learning in Materials Science (3 papers). The work is most often cited by research in Signal Processing (5.5k citations), Artificial Intelligence (9.8k citations), Computer Vision and Pattern Recognition (3.8k citations), Health Informatics (104 citations) and Computational Theory and Mathematics (1.1k citations). George E. Dahl has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Geoffrey E. Hinton, Abdelrahman Mohamed, Tara N. Sainath, Dong Yu, Li Deng, Brian Kingsbury, Navdeep Jaitly, Vincent Vanhoucke, Patrick Nguyen and Andrew Senior. Their work appears in journals such as IEEE Signal Processing Magazine, IEEE Transactions on Audio Speech and Language Processing, Nature Communications, Archives of Pathology & Laboratory Medicine and Journal of Chemical Information and Modeling.
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.