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Cliff Young

Cliff Young has 2 papers in our library, in 2018–2020, most of them on GPU & parallel computing; often with Ashish Vaswani, Dustin Tran, Erich Elsen.

2 papers in our library · 2018–2020

OpenAlex · ORCID · Wikidata · h-index 34 · 127 works · 17,467 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,978 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #4445

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

🏢GoogleMountain View · United States2017–2026 · 19 paperscurrent🏢AlphabetMountain View · United States2026 · 1 papercurrent🏢D. E. Shaw ResearchNew York · United States2007–2014 · 20 papers🎓Harvard UniversityCambridge · United States1994–2009 · 12 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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksComputer Vision and Pattern Recognition · 2 worksHardware and Architecture · 2 worksComputational Theory and Mathematics · 1 workComputer Networks and Communications · 1 work

🧭 Topics 1Research subjects and languages the work is about.GPU & parallel computing · 1 work

 Works by year

2020s

20201 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Sparse GPU Kernels for Deep Learning (2020 · 259 citations)
    Sparse neural networks promise fewer FLOPs and parameters, but on GPUs they rarely run faster than dense ones.
  2. Mesh-TensorFlow: Deep Learning for Supercomputers (2018 · 203 citations)
    Data-parallel training, which splits only the batch across devices, cannot handle models whose parameters exceed a single device's memory.

All works

6 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation ↗arXiv:1609.08144 · no summary yetYonghui Wu et al.2016 · 6,904 citations
  2. Neuromorphic computing at scale ↗Nature · no summary yetDhireesha Kudithipudi et al.2025 · 368 citations
  3. Sparse GPU Kernels for Deep Learning2012 International Conference for High Performance Computing, Networking, Storage and AnalysisTrevor Gale, Matei Zaharia, Cliff Young, et al.2020 · 259 citations
  4. Mesh-TensorFlow: Deep Learning for SupercomputersarXiv:1811.02084Noam Shazeer, Youlong Cheng, Niki Parmar, et al.2018 · 203 citations
  5. A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revolution ↗IEEE Micro · no summary yetJeff Dean, David A. Patterson, Cliff Young2018 · 174 citations
  6. Exploring the limits of Concurrency in ML Training on Google TPUs ↗arXiv (Cornell University) · no summary yetSameer Kumar et al.2020 · 14 citations

 Co-authors here

8 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 1,978 times (of the 149,439 papers whose bibliographies we read); 329 citing papers with a page here →.

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