Johnson Apacible

Computer Vision and Pattern Recognition · 2003–2014

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 14 citations into it; #9,317 of 21,575 by trust here, #17,757 by citations
• Artificial Intelligence — 6 citations into it

Whom Johnson Apacible trusts overall

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

1. Eric Horvitz
2. Yann LeCun
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Matthew D. Zeiler
6. L. Eon Bottou
7. Yoshua Bengio
8. Ilya Sutskever
9. Patrice Y. Simard

Who trusts Johnson Apacible overall

1. Jakub Konecný
2. Brian P. Bailey
3. Shamsi T. Iqbal
4. Joyce Ho
5. Dan Alistarh
6. Aaron Harlap
7. Tal Ben-Nun
8. Eric Horvitz
9. Suyog Gupta
10. J. Konstan

Papers from Johnson Apacible's perspective

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

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

Their papers

• Project Adam: Building an Efficient and Scalable Deep Learning Training System (2014) — 827 citations
• Learning and reasoning about interruption (2003) — 340 citations