Connor Shorten

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 456 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it; #4,617 of 35,345 by trust here, #7,010 by citations
• Computer Vision and Pattern Recognition — 125 citations into it; #4,065 of 21,575 by trust here, #5,116 by citations
• Natural Language Processing — 38 citations into it
• Representation & Self-Supervised Learning — 32 citations into it; #607 of 3,145 by trust here, #939 by citations
• Generative Models — 27 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Model Architectures — 10 citations into it
• Media Technology — 6 citations into it

Whom Connor Shorten trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yoshua Bengio
5. Ilya Sutskever
6. Diederik P. Kingma
7. Karen Simonyan
8. Yann LeCun
9. Christian Szegedy
10. Ross B. Girshick

Who trusts Connor Shorten overall

1. Sergey I. Nikolenko
2. Yuzhen Lu
3. Omid Kashefi
4. Brian Kenji Iwana
5. Jian Liang
6. Azal Ahmad Khan
7. Khaled Alomar
8. Elan Rosenfeld
9. Husam Quteineh
10. Andre da Silva Abade

Papers from Connor Shorten's perspective

Ordered by how strongly the researchers Connor Shorten 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A survey on Image Data Augmentation for Deep Learning (2019) — 11,876 citations
• Text Data Augmentation for Deep Learning (2021) — 632 citations