Jordan Graves

2020

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Whom Jordan Graves trusts overall

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

1. Robert Hecht-Nielsen
2. Florian Schroff
3. Rong Chen
4. Renxiao Wang
5. Kyunghyun Cho
6. J. Jiménez
7. Jianyi Yang
8. Pablo Gainza
9. James Dunbar
10. J. Haas

Papers from Jordan Graves's perspective

Ordered by how strongly the researchers Jordan Graves 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• A Review of Deep Learning Methods for Antibodies (2020) — 84 citations