Frank Seide

9.1k citations
95 papers · 4.3k indexed · 5 hit papers · h-index 24

Frank Seide

88 papers receiving 3.7k citations

Hit Papers

1-bit stochastic gradient descent and its application to ...4392011202620162021100200300400500

Peers

Frank Seide
Comparison fields: 5 of 133
  • Signal Processing 2.1k
  • Artificial Intelligence 3.5k
  • Computational Mathematics 27
  • Computer Vision and Pattern Recognition 765
  • Hardware and Architecture 76
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Citations per field
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Bhuvana Ramabhadran · 1×
Citations per year

Countries citing papers authored by Frank Seide

Since Specialization
Citations

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

Fields of papers citing papers by Frank Seide

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Frank Seide, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Frank Seide Line = papers co-authored together Frank Seide links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20232
3 202210
4
Achieving Human Parity in Conversational Speech Recognition using CNTK and a GPU Farm
20181
5 2017146
6 201528
7
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNsbreakdown →
2014439
8
An introduction to computational networks and the computational network toolkit (invited talk).
20143
9
Recent advances in deep learning for speech research at Microsoftbreakdown →
2013510
10
Conversational speech transcription using context-dependent deep neural networks
201294
11
Conversational speech transcription using context-dependent deep neural networksbreakdown →
2011599
12
Vocabulary and Language Model Adaptation Using just One File
20102
13 20108
14 200638
15
DynaLine: A Non-Disruptive TV User Interface for Passive Browsing of Internet Video
20062
16 200455
17 200316
18 200247
19 199815
20 199719

About Frank Seide

Frank Seide is a scholar working on Signal Processing, Computational Mathematics, Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems and Management, having authored 95 papers that have together received 4.3k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (65 papers), Speech and Audio Processing (37 papers), Music and Audio Processing (35 papers), Natural Language Processing Techniques (33 papers), Speech and dialogue systems (22 papers), Topic Modeling (16 papers), Advanced Data Compression Techniques (5 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Signal Processing (2.1k citations), Artificial Intelligence (3.5k citations), Computational Mathematics (27 citations), Computer Vision and Pattern Recognition (765 citations) and Hardware and Architecture (76 citations). Frank Seide has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Dong Yu, Gang Li, Chen Xie, Kaisheng Yao, Li Deng, Amit Agarwal, Jasha Droppo, Hao Fu, Hang Su and Peng Yu. Their work appears in journals such as IEEE Transactions on Speech and Audio Processing, Speech Communication, IEEE Transactions on Audio Speech and Language Processing, Proceedings of the IEEE and IEEE/ACM Transactions on Audio Speech and Language Processing.

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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