Jay K. Adams

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it; #6,089 of 35,345 by trust here, #5,153 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #4,827 of 21,575 by trust here, #4,001 by citations
• Information Systems — 18 citations into it; #763 of 8,771 by trust here, #416 by citations
• Model Architectures — 9 citations into it
• Recommender Systems — 9 citations into it; #40 of 233 by trust here, #35 by citations
• Natural Language Processing — 7 citations into it

Whom Jay K. Adams trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Jason Aaron Edward Weston
4. Alex Krizhevsky
5. Ilya Sutskever
6. Barret Zoph
7. Quoc V. Le
8. Diederik P. Kingma
9. Frederic Morin
10. Kaiming He

Who trusts Jay K. Adams overall

1. Xiangnan He
2. Guangneng Hu
3. Hongwei Wang
4. Zhe Zhao
5. Fajie Yuan
6. Yongfeng Zhang
7. Aminu Da’u
8. Stephen Bonner
9. Alex Beutel
10. Gabriel Dulac-Arnold

Papers from Jay K. Adams's perspective

Ordered by how strongly the researchers Jay K. Adams 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Neural Networks for YouTube Recommendations (2016) — 3,933 citations
• Neural Input Search for Large Scale Recommendation Models (2019) — 122 citations