Miguel P. Eckstein

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 4 papers; trusts 300 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 86 citations into it; #10,057 of 21,575 by trust here, #17,258 by citations
• Cognitive Neuroscience — 54 citations into it; #3,699 of 10,852 by trust here, #9,453 by citations
• Artificial Intelligence — 24 citations into it
• Cognitive Science & Psychology — 11 citations into it
• Human-Computer Interaction — 10 citations into it
• Reinforcement Learning — 9 citations into it
• Neuroscience — 8 citations into it
• Molecular Biology — 7 citations into it

Whom Miguel P. Eckstein trusts overall

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

1. Laurent Itti
2. B. Tatler
3. Jeremy M. Wolfe
4. Christof Koch
5. Antonio Torralba
6. Claus Bundesen
7. Marvin M. Chun
8. Mary M. Hayhoe
9. Aude Oliva
10. Lingyun Zhang

Who trusts Miguel P. Eckstein overall

1. Shanmugavadivel Karthikeyan
2. Thuyen Ngo
3. Charles Spence
4. Shamik Gupta
5. Bangalore S. Manjunath
6. Yusuke Sugano

Papers from Miguel P. Eckstein's perspective

Ordered by how strongly the researchers Miguel P. Eckstein 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes. (2017) — 80 citations
• Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling (2019) — 47 citations
• Eye tracking assisted extraction of attentionally important objects from videos (2015) — 57 citations
• Visual search: a retrospective. (2011) — 491 citations