Elliot Meyerson

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 5 papers; trusts 515 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 134 citations into it; #5,600 of 35,345 by trust here, #5,317 by citations
• Computer Vision and Pattern Recognition — 80 citations into it
• Model Architectures — 49 citations into it
• Natural Language Processing — 34 citations into it
• Optimization & Training — 27 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Signal Processing — 15 citations into it
• Reinforcement Learning — 14 citations into it; #2,028 of 2,242 by trust here, #2,092 by citations

Whom Elliot Meyerson trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Alex Krizhevsky
7. Ilya Sutskever
8. Jimmy Ba
9. Alex Graves
10. J. Urgen Schmidhuber

Who trusts Elliot Meyerson overall

1. Michael Crawshaw
2. Jorge Armando Mendez Mendez
3. Ferran Alet
4. Andrés Camero
5. Jason Zhi Liang
6. Eric Eaton
7. Ximeng Sun
8. Krzysztof Maziarz
9. Senthil Purushwalkam
10. Haoxuan Qu

Papers from Elliot Meyerson's perspective

Ordered by how strongly the researchers Elliot Meyerson 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Evolving Deep Neural Networks (2017) — 962 citations
• Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering (2017) — 98 citations
• Evolutionary architecture search for deep multitask networks (2018) — 128 citations
• Evolutionary neural AutoML for deep learning (2019) — 120 citations
• Frame Skip Is a Powerful Parameter for Learning to Play Atari (2015) — 80 citations