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 62 Citations 102,753 84 World Ranking 1400 National Ranking 80

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Koray Kavukcuoglu spends much of his time researching Artificial intelligence, Artificial neural network, Reinforcement learning, Deep learning and Machine learning. His Pattern recognition research extends to the thematically linked field of Artificial intelligence. His research on Artificial neural network often connects related areas such as Speech recognition.

The Reinforcement learning study combines topics in areas such as Marginal likelihood and Convolutional neural network. His Deep learning research includes themes of Stability, Active learning and One-shot learning. When carried out as part of a general Unsupervised learning research project, his work on Learning classifier system is frequently linked to work in Learning environment, therefore connecting diverse disciplines of study.

His most cited work include:

  • Human-level control through deep reinforcement learning (11046 citations)
  • Mastering the game of Go with deep neural networks and tree search (7255 citations)
  • Natural Language Processing (Almost) from Scratch (5058 citations)

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

Koray Kavukcuoglu mainly focuses on Artificial intelligence, Artificial neural network, Reinforcement learning, Machine learning and Pattern recognition. His work in Artificial intelligence tackles topics such as Computer vision which are related to areas like Robot. His studies in Artificial neural network integrate themes in fields like Algorithm, Probabilistic logic and Convolutional neural network.

Koray Kavukcuoglu interconnects Mathematical optimization and Human–computer interaction in the investigation of issues within Reinforcement learning. His studies examine the connections between Machine learning and genetics, as well as such issues in Speedup, with regards to Variety. His study in Deep learning is interdisciplinary in nature, drawing from both Stability and One-shot learning.

He most often published in these fields:

  • Artificial intelligence (75.00%)
  • Artificial neural network (37.07%)
  • Reinforcement learning (34.48%)

What were the highlights of his more recent work (between 2017-2020)?

  • Artificial intelligence (75.00%)
  • Reinforcement learning (34.48%)
  • Artificial neural network (37.07%)

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

His primary areas of study are Artificial intelligence, Reinforcement learning, Artificial neural network, Human–computer interaction and Set. His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence. His work on Evolutionary algorithm as part of general Machine learning research is often related to Scheme, Network topology and Architecture, thus linking different fields of science.

His Reinforcement learning study frequently links to adjacent areas such as Quake. His Artificial neural network research integrates issues from Deep learning, Directed acyclic graph and Selection. His study looks at the relationship between Deep learning and fields such as Gradient descent, as well as how they intersect with chemical problems.

Between 2017 and 2020, his most popular works were:

  • Grandmaster level in StarCraft II using multi-agent reinforcement learning. (655 citations)
  • Improved protein structure prediction using potentials from deep learning (518 citations)
  • IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures (440 citations)

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

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Koray Kavukcuoglu mostly deals with Artificial intelligence, Set, Reinforcement learning, Deep learning and Artificial neural network. His research brings together the fields of Random search and Artificial intelligence. Koray Kavukcuoglu integrates many fields in his works, including Set, Sample, Algorithm, Sequence, Softmax function and Pruning.

His Reinforcement learning research includes a combination of various areas of study, such as Key, Scalable distributed, Throughput, Resource and Learning environment. Deep learning connects with themes related to Gradient descent in his study. His work carried out in the field of Artificial neural network brings together such families of science as Generative grammar, Feature learning and Rendering, Computer vision.

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

Human-level control through deep reinforcement learning

Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu.
Nature (2015)

14732 Citations

Mastering the game of Go with deep neural networks and tree search

David Silver;Aja Huang;Christopher J. Maddison;Arthur Guez.
Nature (2016)

10387 Citations

Playing Atari with Deep Reinforcement Learning

Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves.
arXiv: Learning (2013)

6158 Citations

Natural Language Processing (Almost) from Scratch

Ronan Collobert;Jason Weston;Léon Bottou;Michael Karlen.
Journal of Machine Learning Research (2011)

5881 Citations

Asynchronous methods for deep reinforcement learning

Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves.
international conference on machine learning (2016)

4898 Citations

Spatial transformer networks

Max Jaderberg;Karen Simonyan;Andrew Zisserman;Koray Kavukcuoglu.
neural information processing systems (2015)

3273 Citations

WaveNet: A Generative Model for Raw Audio

Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan.
arXiv: Sound (2016)

2177 Citations

What is the best multi-stage architecture for object recognition?

Kevin Jarrett;Koray Kavukcuoglu;Marc'Aurelio Ranzato;Yann LeCun.
international conference on computer vision (2009)

2155 Citations

Torch7: A Matlab-like Environment for Machine Learning

Ronan Collobert;Koray Kavukcuoglu;Clément Farabet.
neural information processing systems (2011)

1761 Citations

Matching networks for one shot learning

Oriol Vinyals;Charles Blundell;Timothy Lillicrap;Koray Kavukcuoglu.
neural information processing systems (2016)

1701 Citations

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Best Scientists Citing Koray Kavukcuoglu

Sergey Levine

Sergey Levine

University of California, Berkeley

Publications: 246

Yoshua Bengio

Yoshua Bengio

University of Montreal

Publications: 208

Pieter Abbeel

Pieter Abbeel

University of California, Berkeley

Publications: 171

Junichi Yamagishi

Junichi Yamagishi

National Institute of Informatics

Publications: 92

Yann LeCun

Yann LeCun

Facebook (United States)

Publications: 92

Trevor Darrell

Trevor Darrell

University of California, Berkeley

Publications: 90

Jianfeng Gao

Jianfeng Gao

Microsoft (United States)

Publications: 89

Nicolas Heess

Nicolas Heess

Google (United States)

Publications: 88

Joshua B. Tenenbaum

Joshua B. Tenenbaum

MIT

Publications: 87

Chelsea Finn

Chelsea Finn

Google (United States)

Publications: 84

Chunhua Shen

Chunhua Shen

University of Adelaide

Publications: 79

Ruslan Salakhutdinov

Ruslan Salakhutdinov

Carnegie Mellon University

Publications: 78

Klaus-Robert Müller

Klaus-Robert Müller

Technical University of Berlin

Publications: 77

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 77

Honglak Lee

Honglak Lee

University of Michigan–Ann Arbor

Publications: 76

Aaron Courville

Aaron Courville

University of Montreal

Publications: 74

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