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 →
This map shows the geographic impact of Li Deng'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 Li Deng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Li Deng more than expected).
This network shows the impact of papers produced by Li Deng. 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 Li Deng. The network helps show where Li Deng may publish in the future.
Co-authorship network of co-authors of Li Deng
This figure shows the co-authorship network connecting the top 25 collaborators of Li Deng.
A scholar is included among the top collaborators of Li Deng 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 Li Deng. Li Deng is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Qiuyuan, Paul Smolensky, Xiaodong He, Li Deng, & Dapeng Wu. (2017). A Neural-Symbolic Approach to Natural Language Tasks.. arXiv (Cornell University).2 indexed citations
Palangi, Hamid, Paul Smolensky, Xiaodong He, & Li Deng. (2017). Deep Learning of Grammatically-Interpretable Representations Through Question-Answering.. arXiv (Cornell University).6 indexed citations
Chen, Jianshu & Li Deng. (2014). A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property. International Conference on Learning Representations.11 indexed citations
9.
He, Xiaodong, Jianfeng Gao, & Li Deng. (2014). Deep Learning for Natural Language Processing: Theory and Practice (Tutorial).3 indexed citations
10.
Vinyals, Oriol, Yangqing Jia, Li Deng, & Trevor Darrell. (2012). Learning with Recursive Perceptual Representations. Neural Information Processing Systems. 25. 2825–2833.45 indexed citations
11.
Yu, Dong & Li Deng. (2011). Deep Learning and Its Applications to Signal and Information Processing. IEEE Signal Processing Magazine.111 indexed citations
12.
He, Xiaodong, Amittai Axelrod, Li Deng, et al.. (2011). The MSR SYSTEM for IWSLT 2011 evaluation.. IWSLT. 57–61.3 indexed citations
13.
Deng, Li, Michael L. Seltzer, Dong Yu, et al.. (2010). Binary coding of speech spectrograms using a deep auto-encoder. 1692–1695.243 indexed citations breakdown →
14.
Baker, James, et al.. (2009). Updated MINDS Report on Speech Recognition and Understanding. IEEE Signal Processing Magazine. 26.16 indexed citations
15.
Yu, Dong, Li Deng, & Shizhen Wang. (2009). Learning in the Deep-Structured Conditional Random Fields. Neural Information Processing Systems.19 indexed citations
16.
Lin, Hui, Li Deng, Jasha Droppo, & Dong Yu. (2008). Learning Methods in Multilingual Speech Recognition. Neural Information Processing Systems.13 indexed citations
17.
Deng, Li & Dong Yu. (2005). A Generative Modeling Framework for Structured Hidden Speech Dynamics. Neural Information Processing Systems.7 indexed citations
18.
Deng, Li. (2003). Luther's Theory of Justification by Faith and its Humanistic Spirit. Journal of Sichuan University.
19.
Frey, Brendan J., Trausti Kristjansson, Li Deng, & Alex Acero. (2001). ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition. Neural Information Processing Systems. 14. 1165–1171.14 indexed citations
20.
Deng, Li, et al.. (1997). Speech adaptation experiments using nonstationary-state HMMs: A MAP approach. International Conference on Acoustics, Speech, and Signal Processing.2 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.