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 32 Citations 40,184 51 World Ranking 7326 National Ranking 3467

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Caglar Gulcehre mainly investigates Artificial intelligence, Recurrent neural network, Artificial neural network, Speech recognition and Machine translation. His studies in Artificial intelligence integrate themes in fields like Computation and Natural language processing. Recurrent neural network and Newton's method are two areas of study in which Caglar Gulcehre engages in interdisciplinary work.

His work on Gradient descent as part of his general Artificial neural network study is frequently connected to Random matrix, Maxima and minima and Saddle point, thereby bridging the divide between different branches of science. His research on Speech recognition frequently links to adjacent areas such as Convolutional neural network. As part of the same scientific family, he usually focuses on Machine translation, concentrating on Phrase and intersecting with Feature, Rule-based machine translation and Evaluation of machine translation.

His most cited work include:

  • Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (6387 citations)
  • Empirical evaluation of gated recurrent neural networks on sequence modeling (4947 citations)
  • Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation (3990 citations)

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

Caglar Gulcehre mainly focuses on Artificial intelligence, Reinforcement learning, Recurrent neural network, Artificial neural network and Machine learning. His Artificial intelligence study frequently draws connections between adjacent fields such as Natural language processing. His study in Reinforcement learning is interdisciplinary in nature, drawing from both Domain, Translation and Human–computer interaction.

His Recurrent neural network research includes themes of Algorithm and Pattern recognition. His Artificial neural network research incorporates elements of Speech recognition and Mathematical optimization. Caglar Gulcehre focuses mostly in the field of Machine translation, narrowing it down to topics relating to Phrase and, in certain cases, Feature.

He most often published in these fields:

  • Artificial intelligence (69.14%)
  • Reinforcement learning (33.33%)
  • Recurrent neural network (29.63%)

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

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Kyunghyun Cho;Bart van Merrienboer;Caglar Gulcehre;Dzmitry Bahdanau.
arXiv: Computation and Language (2014)

5746 Citations

Empirical evaluation of gated recurrent neural networks on sequence modeling

Junyoung Chung;Çaglar Gülçehre;KyungHyun Cho;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio.
arXiv: Neural and Evolutionary Computing (2014)

5599 Citations

Theano: A Python framework for fast computation of mathematical expressions

Rami Al-Rfou;Guillaume Alain;Amjad Almahairi.
arXiv: Symbolic Computation (2016)

1962 Citations

Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

Ramesh Nallapati;Bowen Zhou;Cicero Nogueira dos santos;Caglar Gulcehre.
conference on computational natural language learning (2016)

962 Citations

Relational inductive biases, deep learning, and graph networks

Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez.
arXiv: Learning (2018)

873 Citations

Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Oriol Vinyals;Igor Babuschkin;Wojciech M. Czarnecki;Michaël Mathieu.
Nature (2019)

802 Citations

Identifying and attacking the saddle point problem in high-dimensional non-convex optimization

Yann N Dauphin;Razvan Pascanu;Caglar Gulcehre;Kyunghyun Cho.
arXiv: Learning (2014)

609 Citations

How to Construct Deep Recurrent Neural Networks

Razvan Pascanu;Caglar Gulcehre;Kyunghyun Cho;Yoshua Bengio.
international conference on learning representations (2014)

498 Citations

On using monolingual corpora in neural machine translation

Çaglar Gülçehre;Orhan Firat;Kelvin Xu;Kyunghyun Cho.
arXiv: Computation and Language (2015)

493 Citations

Gated Feedback Recurrent Neural Networks

Junyoung Chung;Caglar Gulcehre;Kyunghyun Cho;Yoshua Bengio;Yoshua Bengio.
arXiv: Neural and Evolutionary Computing (2015)

336 Citations

Best Scientists Citing Caglar Gulcehre

Yoshua Bengio

Yoshua Bengio

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Publications: 141

Kyunghyun Cho

Kyunghyun Cho

New York University

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Xipeng Qiu

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Maosong Sun

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Tsinghua University

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Aaron Courville

Aaron Courville

University of Montreal

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Caiming Xiong

Caiming Xiong

Salesforce (United States)

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Yang Liu

Yang Liu

Tsinghua University

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Graham Neubig

Graham Neubig

Carnegie Mellon University

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Zhe Gan

Zhe Gan

Microsoft (United States)

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Min Zhang

Min Zhang

Tsinghua University

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Xuanjing Huang

Xuanjing Huang

Fudan University

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Yi Yang

Yi Yang

Zhejiang University

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Richard Socher

Richard Socher

you.com

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Mohit Bansal

Mohit Bansal

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Mirella Lapata

Mirella Lapata

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Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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