Kenton Murray

Artificial Intelligence · with Chiang · 2015–2018

A researcher in the Science corpus: 3 papers; trusts 86 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #5,958 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 30 citations into it; #1,328 of 5,688 by trust here, #2,930 by citations
• Computer Vision and Pattern Recognition — 21 citations into it; #9,342 of 21,575 by trust here, #15,955 by citations
• Optimization & Training — 13 citations into it
• Model Architectures — 7 citations into it

Whom Kenton Murray trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Franz Joseph Och
4. Yann LeCun
5. John C. Duchi
6. Philipp Koehn
7. J. Urgen Schmidhuber
8. Alex Krizhevsky
9. Ilya Sutskever
10. Dzmitry Bahdanau

Who trusts Kenton Murray overall

1. Felix Stahlberg
2. Clara Meister
3. Bryan Eikema
4. Angela Fan
5. Lorenz Kuhn
6. Paul Michel
7. Ryan Cotterell
8. Julian Salazar
9. Prasann Singhal
10. Tsubasa Ochiai

Papers from Kenton Murray's perspective

Ordered by how strongly the researchers Kenton Murray trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Correcting Length Bias in Neural Machine Translation (2018) — 197 citations
• Auto-Sizing Neural Networks: With Applications to n-gram Language Models (2015) — 47 citations
• An Empirical Exploration of Curriculum Learning for Neural Machine Translation (2018) — 125 citations