Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
20126.6k citationsGeoffrey E. Hinton, Li Deng et al.IEEE Signal Processing Magazineprofile →
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
Countries citing papers authored by George E. Dahl
Since
Specialization
Citations
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).
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-authorship network of co-authors of George E. Dahl
This figure shows the co-authorship network connecting the top 25 collaborators of George E. Dahl.
A scholar is included among the top collaborators of George E. Dahl based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with George E. Dahl. George E. Dahl is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Faber, Felix A., Bing Huang, Justin Gilmer, et al.. (2017). Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy. arXiv (Cornell University).5 indexed citations
4.
Faber, Felix A., Luke A. D. Hutchison, Bing Huang, et al.. (2017). Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. Journal of Chemical Theory and Computation. 13(11). 5255–5264.459 indexed citations breakdown →
5.
Ma, Junshui, Robert P. Sheridan, Andy Liaw, George E. Dahl, & Vladimir Svetnik. (2015). Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships. Journal of Chemical Information and Modeling. 55(2). 263–274.787 indexed citations breakdown →
6.
Sainath, Tara N., Brian Kingsbury, George Saon, et al.. (2014). Deep Convolutional Neural Networks for Large-scale Speech Tasks. Neural Networks. 64. 39–48.364 indexed citations breakdown →
Dahl, George E., Tara N. Sainath, & Geoffrey E. Hinton. (2013). Improving deep neural networks for LVCSR using rectified linear units and dropout. 8609–8613.933 indexed citations breakdown →
9.
Dahl, George E., Jack W. Stokes, Li Deng, & Dong Yu. (2013). Large-scale malware classification using random projections and neural networks. 3422–3426.270 indexed citations breakdown →
Hinton, Geoffrey E., Li Deng, Dong Yu, et al.. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine. 29(6). 82–97.6614 indexed citations breakdown →
14.
Dahl, George E., Marc’Aurelio Ranzato, Abdelrahman Mohamed, & Geoffrey E. Hinton. (2010). Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. Neural Information Processing Systems. 23. 469–477.185 indexed citations
15.
Yu, Dong, Li Deng, & George E. Dahl. (2010). Roles of Pre-Training and Fine-Tuning in Context-Dependent DBN-HMMs for Real-World Speech Recognition. Neural Information Processing Systems.133 indexed citations
Adams, Ryan P., George E. Dahl, & Iain Murray. (2010). Incorporating side information into probabilistic matrix factorization using Gaussian Processes. ERA.10 indexed citations
18.
Dahl, George E., et al.. (2008). Parallelizing neural network training for cluster systems. Works - Scholarship, Research, & Creative Expression (Swarthmore College). 220–225.20 indexed citations
19.
Dahl, George E., et al.. (2007). SW-AG. 304–307.6 indexed citations
20.
Dahl, George E., et al.. (1971). Los peces del norte de Colombia.140 indexed citations
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.