World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
38
Citations
58051
World Ranking
708
National Ranking
38

Computer Science

D-Index
38
Citations
62545
World Ranking
9918
National Ranking
621

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Caglar Gulcehre is a researcher affiliated with DeepMind in the United Kingdom. Their research contributions primarily lie within the field of Computer Science, with a focus on areas such as Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Computer Networks and Communications, and Control and Systems Engineering.

The scientist's work covers several main topics, including:

  • Reinforcement Learning in Robotics
  • Machine Learning and Algorithms
  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Bandit Algorithms Research
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications

Caglar Gulcehre has authored numerous publications, predominantly appearing in the venue arXiv (Cornell University), with a total of 39 publications in this outlet. Some recent papers include:

  • Understanding the Impact of Value Selection Heuristics in Scheduling Problems, 2025, arXiv (Cornell University)
  • Static Analysis of Shape in TensorFlow Programs, 2020, arXiv (Cornell University)
  • Critic Regularized Regression, 2020, arXiv (Cornell University)
  • Acme: A Research Framework for Distributed Reinforcement Learning, 2020, arXiv (Cornell University)
  • Assessing Factoid Question-Answer Generation for Portuguese (Short Paper), 2020, arXiv (Cornell University)

The number of citations for some papers highlights their impact within the research community, such as 2380 citations for work on value selection heuristics and 1669 for static analysis in TensorFlow programs.

Frequent collaborators in their work include Razvan Pascanu, Nando de Freitas, Konrad Żołna, Tom Le Paine, and Ziyu Wang, with collaboration counts ranging from 5 to 10 joint publications.

Best Publications

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

    Kyunghyun Cho;Bart van Merrienboer;Caglar Gulcehre;Dzmitry Bahdanau

  • Empirical evaluation of gated recurrent neural networks on sequence modeling

    Junyoung Chung;Çaglar Gülçehre;KyungHyun Cho;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio

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

    Oriol Vinyals;Igor Babuschkin;Wojciech M. Czarnecki;Michaël Mathieu

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

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

    Ramesh Nallapati;Bowen Zhou;Cicero Nogueira dos santos;Caglar Gulcehre

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

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

    Yann N Dauphin;Razvan Pascanu;Caglar Gulcehre;Kyunghyun Cho

  • How to Construct Deep Recurrent Neural Networks

    Razvan Pascanu;Caglar Gulcehre;Kyunghyun Cho;Yoshua Bengio

  • Gated Feedback Recurrent Neural Networks

    Junyoung Chung;Caglar Gulcehre;Kyunghyun Cho;Yoshua Bengio;Yoshua Bengio

  • On using monolingual corpora in neural machine translation

    Çaglar Gülçehre;Orhan Firat;Kelvin Xu;Kyunghyun Cho

  • Pointing the unknown words

    Caglar Gulcehre;Sungjin Ahn;Ramesh Nallapati;Bowen Zhou

  • EmoNets: Multimodal deep learning approaches for emotion recognition in video

    Samira Ebrahimi Kahou;Xavier Bouthillier;Pascal Lamblin;Çaglar Gülçehre

  • Combining modality specific deep neural networks for emotion recognition in video

    Samira Ebrahimi Kahou;Christopher Pal;Xavier Bouthillier;Pierre Froumenty

  • Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

    Iulian Vlad Serban;Alberto García-Durán;Çaglar Gülçehre;Sungjin Ahn

  • Recurrent Batch Normalization

    Tim Cooijmans;Nicolas Ballas;César Laurent;Çaglar Gülçehre

  • Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

    Natasha Jaques;Angeliki Lazaridou;Edward Hughes;Çaglar Gülçehre

  • Noisy activation functions

    Caglar Gulcehre;Marcin Moczulski;Misha Denil;Yoshua Bengio

  • Learned-norm pooling for deep feedforward and recurrent neural networks

    Caglar Gulcehre;Kyunghyun Cho;Razvan Pascanu;Yoshua Bengio

  • Stabilizing Transformers for Reinforcement Learning

    Emilio Parisotto;Francis Song;Jack Rae;Razvan Pascanu

  • Machine Comprehension by Text-to-Text Neural Question Generation

    Xingdi Yuan;Tong Wang;Caglar Gulcehre;Alessandro Sordoni

  • Critic Regularized Regression

    Ziyu Wang;Alexander Novikov;Konrad Zolna;Jost Tobias Springenberg

  • Acme: A Research Framework for Distributed Reinforcement Learning

    Matt Hoffman;Bobak Shahriari;John Aslanides;Gabriel Barth-Maron

  • Machine Comprehension by Text-to-Text Neural Question Generation

    Xingdi Yuan;Tong Wang;Caglar Gulcehre;Alessandro Sordoni

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Kyunghyun Cho
Kyunghyun Cho New York University
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Aaron Courville
Aaron Courville University of Montreal
Adam Trischler
Adam Trischler Microsoft (United States)
Yann N. Dauphin
Yann N. Dauphin Google (United States)
Bowen Zhou
Bowen Zhou IBM (United States)
Raia Hadsell
Raia Hadsell DeepMind (United Kingdom)

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