Abdelrahman Mohamed

38.1k total citations · 13 hit papers
52 papers, 21.6k citations indexed

About

Abdelrahman Mohamed is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Abdelrahman Mohamed has authored 52 papers receiving a total of 21.6k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 32 papers in Signal Processing and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Abdelrahman Mohamed's work include Speech Recognition and Synthesis (38 papers), Music and Audio Processing (31 papers) and Speech and Audio Processing (26 papers). Abdelrahman Mohamed is often cited by papers focused on Speech Recognition and Synthesis (38 papers), Music and Audio Processing (31 papers) and Speech and Audio Processing (26 papers). Abdelrahman Mohamed collaborates with scholars based in United States, Canada and Israel. Abdelrahman Mohamed's co-authors include Geoffrey E. Hinton, Alex Graves, George E. Dahl, Navdeep Jaitly, Dong Yu, Li Deng, Tara N. Sainath, Brian Kingsbury, Vincent Vanhoucke and Patrick Nguyen and has published in prestigious journals such as Nature Neuroscience, IEEE Signal Processing Magazine and Neural Networks.

In The Last Decade

Abdelrahman Mohamed

50 papers receiving 20.2k citations

Hit Papers

Deep Neural Networks for ... 2010 2026 2015 2020 2012 2013 2014 2011 2012 2.0k 4.0k 6.0k
What are hit papers?

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.

  1. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
    2012 6.6k citations Geoffrey E. Hinton, Li Deng et al. IEEE Signal Processing Magazine profile →
  2. Speech recognition with deep recurrent neural networks
    2013 5.6k citations Alex Graves, Abdelrahman Mohamed et al. profile →
  3. Convolutional Neural Networks for Speech Recognition
    2014 1.6k citations Abdelrahman Mohamed, Li Deng et al. profile →
  4. Acoustic Modeling Using Deep Belief Networks
    2011 1.2k citations Abdelrahman Mohamed, George E. Dahl et al. profile →
  5. Deep Neural Networks for Acoustic Modeling in Speech Recognition
    2012 1.2k citations Geoffrey E. Hinton, Li Deng et al. IEEE Signal Processing Magazine profile →
  6. Hybrid speech recognition with Deep Bidirectional LSTM
    2013 1.1k citations Alex Graves, Navdeep Jaitly et al. profile →
  7. Deep convolutional neural networks for LVCSR
    2013 723 citations Tara N. Sainath, Abdelrahman Mohamed et al. profile →
  8. Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition
    2012 593 citations Abdelrahman Mohamed et al. profile →
  9. SUPERB: Speech Processing Universal PERformance Benchmark
    2021 409 citations Kushal Lakhotia, Abdelrahman Mohamed et al. profile →
  10. Deep Convolutional Neural Networks for Large-scale Speech Tasks
    2014 364 citations Tara N. Sainath, Brian Kingsbury et al. profile →
  11. Binary coding of speech spectrograms using a deep auto-encoder
    2010 243 citations Li Deng, Michael L. Seltzer et al. profile →
  12. Deep Belief Networks using discriminative features for phone recognition
    2011 196 citations Abdelrahman Mohamed, Tara N. Sainath et al. profile →
  13. Understanding how Deep Belief Networks perform acoustic modelling
    2012 186 citations Abdelrahman Mohamed, Geoffrey E. Hinton et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Abdelrahman Mohamed United States 29 13.1k 7.7k 4.5k 2.0k 1.1k 52 21.6k
Andrew Senior United States 41 11.1k 0.8× 7.0k 0.9× 5.3k 1.2× 1.9k 0.9× 791 0.7× 89 21.7k
Dong Yu United States 61 17.8k 1.4× 12.2k 1.6× 5.1k 1.1× 1.8k 0.9× 1.1k 1.0× 375 26.9k
George E. Dahl United States 21 9.8k 0.8× 5.5k 0.7× 3.8k 0.8× 1.5k 0.7× 655 0.6× 25 17.6k
Tara N. Sainath United States 48 11.5k 0.9× 7.6k 1.0× 3.2k 0.7× 1.4k 0.7× 687 0.6× 180 17.5k
Li Deng United States 69 19.3k 1.5× 9.8k 1.3× 7.8k 1.7× 2.0k 1.0× 1.1k 1.0× 316 30.4k
Navdeep Jaitly United States 34 10.1k 0.8× 5.8k 0.8× 2.8k 0.6× 1.1k 0.5× 492 0.5× 62 16.7k
Brian Kingsbury United States 38 9.3k 0.7× 6.0k 0.8× 2.7k 0.6× 1.1k 0.6× 559 0.5× 137 14.1k
Nitish Srivastava United States 15 10.4k 0.8× 2.2k 0.3× 7.6k 1.7× 2.1k 1.0× 1.3k 1.2× 21 25.0k
Quoc V. Le United States 64 18.0k 1.4× 4.1k 0.5× 13.4k 3.0× 1.7k 0.8× 655 0.6× 135 31.6k
Jieping Ye United States 87 7.9k 0.6× 2.3k 0.3× 8.7k 1.9× 1.7k 0.8× 1.4k 1.3× 439 25.3k

Countries citing papers authored by Abdelrahman Mohamed

Since Specialization
Citations

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

Fields of papers citing papers by Abdelrahman Mohamed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abdelrahman Mohamed

This figure shows the co-authorship network connecting the top 25 collaborators of Abdelrahman Mohamed. A scholar is included among the top collaborators of Abdelrahman Mohamed 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 Abdelrahman Mohamed. Abdelrahman Mohamed is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mohamed, Abdelrahman, et al.. (2024). Self-Supervised Models of Speech Infer Universal Articulatory Kinematics. 33. 12061–12065. 2 indexed citations
2.
Kharitonov, Eugene, Jade Copet, Yossi Adi, et al.. (2023). Generative Spoken Dialogue Language Modeling. Transactions of the Association for Computational Linguistics. 11. 250–266. 20 indexed citations
3.
Tomasello, Paden, Po‐Chun Hsu, Duc Van Le, et al.. (2023). Stop: A Dataset for Spoken Task Oriented Semantic Parsing. 991–998. 9 indexed citations
4.
Polyak, Adam, Yossi Adi, Jade Copet, et al.. (2021). Speech Resynthesis from Discrete Disentangled Self-Supervised Representations. arXiv (Cornell University). 3615–3619. 144 indexed citations
5.
Hsu, Wei-Ning, Yao-Hung Hubert Tsai, Benjamin Bolte, Ruslan Salakhutdinov, & Abdelrahman Mohamed. (2021). Hubert: How Much Can a Bad Teacher Benefit ASR Pre-Training?. 6533–6537. 83 indexed citations
6.
Baevski, Alexei, Yuhao Zhou, Abdelrahman Mohamed, & Michael Auli. (2020). wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. Neural Information Processing Systems. 33. 12449–12460. 155 indexed citations
7.
Mohamed, Abdelrahman, et al.. (2020). Enhancing Customer Service Using Chatbot Application Through Artificial Intelligence. Journal of Computational and Theoretical Nanoscience. 17(4). 1633–1637. 3 indexed citations
8.
Wang, Chong, Yining Wang, Po-Sen Huang, et al.. (2017). SEQUENCE MODELING VIA SEGMENTATIONS. International Conference on Machine Learning. 3674–3683. 11 indexed citations
9.
Wang, Shengjie, Abdelrahman Mohamed, Rich Caruana, et al.. (2016). Analysis of Deep Neural Networks with Extended Data Jacobian Matrix. International Conference on Machine Learning. 718–726. 8 indexed citations
10.
Li, Jinyu, Abdelrahman Mohamed, Geoffrey Zweig, & Yifan Gong. (2015). LSTM time and frequency recurrence for automatic speech recognition. 187–191. 72 indexed citations
11.
Geras, Krzysztof J., Abdelrahman Mohamed, Rich Caruana, et al.. (2015). Compressing LSTMs into CNNs.. arXiv (Cornell University). 3 indexed citations
12.
Sainath, Tara N., Abdelrahman Mohamed, Brian Kingsbury, & Bhuvana Ramabhadran. (2013). Deep convolutional neural networks for LVCSR. 8614–8618. 723 indexed citations breakdown →
13.
Graves, Alex, Navdeep Jaitly, & Abdelrahman Mohamed. (2013). Hybrid speech recognition with Deep Bidirectional LSTM. 273–278. 1138 indexed citations breakdown →
14.
Hinton, Geoffrey E., Li Deng, Dong Yu, et al.. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition. IEEE Signal Processing Magazine. 29(6). 82–97. 1169 indexed citations breakdown →
15.
Tang, Yichuan & Abdelrahman Mohamed. (2012). Multiresolution Deep Belief Networks. International Conference on Artificial Intelligence and Statistics. 1203–1211. 12 indexed citations
16.
Hinton, Geoffrey E., Li Deng, Dong Yu, et al.. (2012). The shared views of four research groups ). 1 indexed citations
17.
Sainath, Tara N., Brian Kingsbury, Bhuvana Ramabhadran, et al.. (2011). Making Deep Belief Networks effective for large vocabulary continuous speech recognition. 30–35. 150 indexed citations
18.
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
19.
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 →
20.
Mohamed, Abdelrahman & Geoffrey E. Hinton. (2010). Phone recognition using Restricted Boltzmann Machines. 4354–4357. 64 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.

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