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Manjunath Kudlur

Manjunath Kudlur has 2 papers in our library, in 2015–2016; often with Andy Davis, Geoffrey Irving, Jeff Dean.

2 papers in our library · 2015–2016

OpenAlex · h-index 21 · 54 works · 23,527 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,342 times in our library · ⭐ Paper Stars ranking: #232

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

🎓University of MichiganAnn Arbor · United States2004–2009 · 21 papers🏢NvidiaSanta Clara · United States2009–2012 · 7 papers🏢GoogleMountain View · United States2015–2017 · 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksComputer Networks and Communications · 1 workHardware and Architecture · 1 workInformation Systems · 1 workArtificial Intelligence · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Machine learning · 1 work

 Works by year

2010s

20161 paper20151 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TensorFlow: A system for large-scale machine learning (2016 · 12,711 citations)
    The paper presents TensorFlow, Google Brain's successor to its earlier DistBelief system. It addresses a gap among existing tools. Single-machine frameworks such as Caffe, Theano and Torch are flexible but do not scale across clusters. Parameter-server systems scale, but they hard-code how shared model state is…
  2. Order Matters: Sequence to sequence for sets (2015 · 245 citations)
    Seq2seq models assume that inputs and outputs arrive as ordered sequences. Many problems, such as sorting a collection of numbers or predicting the joint distribution of random variables, involve data with no obvious order. The authors show that the chosen ordering can strongly affect how well such models learn, even…

 Co-authors here

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

 Cited by

Papers of our library cite their works 1,342 times (of the 149,439 papers whose bibliographies we read); 102 citing papers with a page here →.

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