Will Monroe

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 7 papers; trusts 396 researchers and is trusted by 302 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 167 citations into it; #1,150 of 35,345 by trust here, #1,596 by citations
• Natural Language Processing — 132 citations into it; #572 of 5,688 by trust here, #481 by citations
• Computer Vision and Pattern Recognition — 65 citations into it; #20,327 of 21,575 by trust here, #4,030 by citations
• Model Architectures — 30 citations into it
• Reinforcement Learning — 27 citations into it; #1,216 of 2,242 by trust here, #384 by citations
• Generative Models — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Optimization & Training — 12 citations into it

Whom Will Monroe trusts overall

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

1. Ilya Sutskever
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Oriol Vinyals
5. Kyunghyun Cho
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Dzmitry Bahdanau
9. Matthew D. Zeiler
10. Yann LeCun

Who trusts Will Monroe overall

1. Yonatan Belinkov
2. Jianfeng Gao
3. Kyunghyun Cho
4. Jiwei Li
5. Mohit Bansal
6. Eric Wallace
7. Ilia Kulikov
8. Wojciech Samek
9. Percy Liang
10. Natasha Jaques

Papers from Will Monroe's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep Reinforcement Learning for Dialogue Generation (2016) — 1,424 citations
• Adversarial Learning for Neural Dialogue Generation (2017) — 918 citations
• A Simple, Fast Diverse Decoding Algorithm for Neural Generation (2016) — 251 citations
• Understanding Neural Networks through Representation Erasure (2016) — 626 citations
• Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding (2017) — 145 citations
• Fitting In or Standing Out? The Tradeoffs of Structural and Cultural Embeddedness (2015) — 195 citations
• Data Distillation for Controlling Specificity in Dialogue Generation (2017) — 22 citations