D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 79 Citations 188,320 163 World Ranking 470 National Ranking 282

Research.com Recognitions

Awards & Achievements

2021 - IEEE John von Neumann Medal “For contributions to the science and engineering of large-scale distributed computer systems and artificial intelligence systems.”

2016 - Fellow of the American Academy of Arts and Sciences

2013 - Fellow of the American Association for the Advancement of Science (AAAS)

2012 - ACM Prize in Computing For their leadership in the science and engineering of Internet-scale distributed systems.

2009 - Member of the National Academy of Engineering For contributions to the science and engineering of large-scale distributed computer systems.

2009 - ACM Fellow For contributions to the science and engineering of large-scale distributed computer systems.

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Artificial intelligence
  • Programming language

His primary areas of investigation include Artificial intelligence, Machine learning, Artificial neural network, Deep learning and Word embedding. His Artificial intelligence research integrates issues from Pattern recognition, Vocabulary and Natural language processing. His work deals with themes such as Ensemble learning and Acoustic model, which intersect with Artificial neural network.

His Deep learning research incorporates elements of Context, CUDA, Distributed computing and Reinforcement learning. As a part of the same scientific study, Jeffrey Dean usually deals with the Word embedding, concentrating on Word2vec and frequently concerns with Syntax. His work on Distributional semantics as part of general Word study is frequently linked to Simple, bridging the gap between disciplines.

His most cited work include:

  • MapReduce: simplified data processing on large clusters (16372 citations)
  • Distributed Representations of Words and Phrases and their Compositionality (13085 citations)
  • Efficient Estimation of Word Representations in Vector Space (10850 citations)

What are the main themes of his work throughout his whole career to date?

Artificial intelligence, Information retrieval, Machine learning, World Wide Web and Artificial neural network are his primary areas of study. His Artificial intelligence study combines topics from a wide range of disciplines, such as Pattern recognition and Natural language processing. The concepts of his Information retrieval study are interwoven with issues in Set and Database.

Jeffrey Dean has included themes like Computation, Inference and Dataflow in his Machine learning study. The study incorporates disciplines such as Language model and Speech recognition in addition to Machine translation. Word embedding is a primary field of his research addressed under Word.

He most often published in these fields:

  • Artificial intelligence (29.21%)
  • Information retrieval (21.78%)
  • Machine learning (13.86%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (29.21%)
  • Machine learning (13.86%)
  • Deep learning (9.41%)

In recent papers he was focusing on the following fields of study:

Jeffrey Dean mainly investigates Artificial intelligence, Machine learning, Deep learning, Artificial neural network and Machine translation. His Reinforcement learning study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Health informatics, bridging the gap between disciplines. His Machine learning study combines topics in areas such as SIGNAL, Computation, Inference and Dataflow.

His work carried out in the field of Deep learning brings together such families of science as Routing, Context and Data science. Jeffrey Dean has researched Artificial neural network in several fields, including Language model, Natural language understanding and Human–computer interaction. His Machine translation research includes themes of Sentence and Natural language.

Between 2015 and 2021, his most popular works were:

  • TensorFlow: a system for large-scale machine learning (4961 citations)
  • Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2967 citations)
  • TensorFlow: A system for large-scale machine learning (2113 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Artificial intelligence
  • Programming language

His scientific interests lie mostly in Artificial intelligence, Machine learning, Deep learning, Artificial neural network and Machine translation. His research brings together the fields of Key and Artificial intelligence. His research investigates the link between Machine learning and topics such as Computation that cross with problems in Dataflow, Multi-core processor and CUDA.

His biological study spans a wide range of topics, including Data point, Statistical model and Interoperability. Jeffrey Dean combines subjects such as Graph and Set with his study of Artificial neural network. His Machine translation course of study focuses on Sentence and Speech recognition.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

MapReduce: simplified data processing on large clusters

Jeffrey Dean;Sanjay Ghemawat.
Communications of The ACM (2008)

25334 Citations

Distributed Representations of Words and Phrases and their Compositionality

Tomas Mikolov;Ilya Sutskever;Kai Chen;Greg S Corrado.
neural information processing systems (2013)

10675 Citations

Efficient Estimation of Word Representations in Vector Space

Tomas Mikolov;Kai Chen;Greg S. Corrado;Jeffrey Dean.
arXiv: Computation and Language (2013)

8235 Citations

Bigtable: A Distributed Storage System for Structured Data

Fay Chang;Jeffrey Dean;Sanjay Ghemawat;Wilson C. Hsieh.
ACM Transactions on Computer Systems (2008)

7013 Citations

Distilling the Knowledge in a Neural Network

Geoffrey E. Hinton;Oriol Vinyals;Jeffrey Dean.
arXiv: Machine Learning (2015)

6850 Citations

TensorFlow: a system for large-scale machine learning

Martín Abadi;Paul Barham;Jianmin Chen;Zhifeng Chen.
operating systems design and implementation (2016)

6287 Citations

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo.
arXiv: Distributed, Parallel, and Cluster Computing (2015)

6037 Citations

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Yonghui Wu;Mike Schuster;Zhifeng Chen;Quoc V. Le.
arXiv: Computation and Language (2016)

3316 Citations

Large Scale Distributed Deep Networks

Jeffrey Dean;Greg Corrado;Rajat Monga;Kai Chen.
neural information processing systems (2012)

2850 Citations

Building high-level features using large scale unsupervised learning

Marc'aurelio Ranzato;Rajat Monga;Matthieu Devin;Kai Chen.
international conference on machine learning (2012)

2332 Citations

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