Eric Granger

Computer Vision and Pattern Recognition · 2007–2018

A researcher in the Science corpus: 4 papers; trusts 188 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 66 citations into it; #19,514 of 21,575 by trust here, #15,162 by citations
• Artificial Intelligence — 56 citations into it; #31,781 of 35,345 by trust here, #15,866 by citations
• Safety, Robustness & Interpretability — 16 citations into it
• Model Architectures — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Statistical Learning Theory — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Eric Granger trusts overall

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

1. Gail A. Carpenter
2. M. Stone
3. Patrick Grother
4. Geoffrey E. Hinton
5. Stephen Grossberg
6. David E. Rumelhart
7. Yoshua Bengio
8. Alex Krizhevsky
9. Kaiming He
10. Yann LeCun

Who trusts Eric Granger overall

1. Gregory Cohen
2. Mingxia Liu
3. Yoshua Bengio
4. Saeed Afshar

Papers from Eric Granger's perspective

Ordered by how strongly the researchers Eric Granger 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Supervised learning of fuzzy ARTMAP neural networks through particle swarm optimization (2007) — 32 citations
• Multiple instance learning: A survey of problem characteristics and applications (2016) — 750 citations
• Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses (2018) — 327 citations
• Incremental learning of privacy-preserving Bayesian networks (2013) — 19 citations