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Gregory Diamos

Gregory Diamos has 3 papers in our library, in 2014–2017, most of them on Speech & spoken language, English; often with Adam Coates, Bryan Catanzaro, Carl Case.

3 papers in our library · 2014–2017

OpenAlex · h-index 34 · 75 works · 8,749 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 516 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #730

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓The University of Texas at DallasRichardson · United States2026 · 1 papercurrent🏢BaiduBeijing · China2016–2021 · 14 papers🎓Georgia Institute of TechnologyAtlanta · United States2008–2012 · 14 papers🏢NvidiaSanta Clara · United States2012–2014 · 8 papers🏢NvidiaReading · United Kingdom2012–2014 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Speech & spoken language · 2 worksEnglish · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Zipf's law · 1 work

 Works by year

2010s

20171 paper20151 paper20141 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin (2015 · 2,341 citations)
    Conventional speech recognizers are assembled from many hand-tuned parts, including feature extractors, acoustic, pronunciation and language models, and speaker adaptation.
  2. Deep Speech: Scaling up end-to-end speech recognition (2014 · 1,600 citations)
    Conventional speech recognizers chain together many hand-built parts: engineered input features, acoustic models, HMMs, phoneme dictionaries, and extra modules for noise and speaker adaptation.
  3. Deep Learning Scaling is Predictable, Empirically (2017 · 518 citations)
    Deep learning usually advances through three levers: better architectures, larger training sets and more compute.

All works

7 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Deep Speech 2: End-to-End Speech Recognition in English and MandarinarXiv:1512.02595Dario Amodei, Rishita Anubhai, Eric Battenberg, et al.2015 · 2,341 citations
  2. Deep Speech: Scaling up end-to-end speech recognitionarXiv:1412.5567Awni Hannun, Carl Case, Jared Casper, et al.2014 · 1,600 citations
  3. Deep Learning Scaling is Predictable, EmpiricallyarXiv:1712.00409Joel Hestness, Sharan Narang, Newsha Ardalani, et al.2017 · 518 citations
  4. Language Modeling at Scale ↗arXiv:1810.10045 · no summary yetMostofa Patwary; Milind Chabbi; Heewoo Jun; Jiaji Huang; Gregory Diamos;, Kenneth Church2018 · 6 citations
  5. OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks ↗arXiv (Cornell University) · no summary yetFarbod Tavakkoli et al.2026
  6. OTel: Open Telco AI Datasets, Benchmarks, and Models ↗arXiv (Cornell University) · no summary yetFarbod Tavakkoli et al.2026
  7. A Proposed Hierarchy of Deep Learning Tasks ↗no summary yetJoel Hestness et al.2018

 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 516 times (of the 149,439 papers whose bibliographies we read); 77 citing papers with a page here →.

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