Lynne E. Parker

Computer Vision and Pattern Recognition · 2000–2017

A researcher in the Science corpus: 9 papers; trusts 429 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 87 citations into it; #4,334 of 21,575 by trust here, #8,296 by citations
• Computer Networks and Communications — 53 citations into it; #1,820 of 9,886 by trust here, #2,761 by citations
• Artificial Intelligence — 46 citations into it; #21,554 of 35,345 by trust here, #32,362 by citations
• Mechanical Engineering — 39 citations into it; #865 of 8,300 by trust here, #3,653 by citations
• Biomedical Engineering — 22 citations into it; #512 of 10,734 by trust here, #5,019 by citations
• Aerospace Engineering — 19 citations into it
• Control and Systems Engineering — 16 citations into it
• Electrical and Electronic Engineering — 15 citations into it

Whom Lynne E. Parker trusts overall

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

1. Ivan Laptev
2. Tucker R. Balch
3. Lovekesh Vig
4. Sebastian Thrun
5. Brian P. Gerkey
6. David G. Lowe
7. Geoffrey E. Hinton
8. Chih-Jen Lin
9. Andrew Zisserman
10. Robert M. Harlan

Who trusts Lynne E. Parker overall

1. Zhihua Qu
2. Fei Han
3. Hao Zhang
4. Hema Swetha Koppula
5. Richard M. Murray
6. Francesco Bullo
7. Jing Wang
8. Lu Xia
9. Yan Song
10. Chenyang Zhang

Papers from Lynne E. Parker's perspective

Ordered by how strongly the researchers Lynne E. Parker 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• 4-dimensional local spatio-temporal features for human activity recognition (2011) — 155 citations
• Simplex-Based 3D Spatio-temporal Feature Description for Action Recognition (2014) — 42 citations
• Bio-inspired predictive orientation decomposition of skeleton trajectories for real-time human activity prediction (2015) — 26 citations
• Minimum uncertainty latent variable models for robot recognition of sequential human activities (2017)
• Multiple Mobile Robot Systems (2016) — 316 citations
• Distributed Intelligence: Overview of the Field and Its Application in Multi-Robot Systems (2008) — 300 citations
• Experiments with a Large Heterogeneous Mobile Robot Team: Exploration, Mapping, Deployment and Detection (2006) — 330 citations
• Guest editorial advances in multirobot systems (2002) — 407 citations
• Current State of the Art in Distributed Autonomous Mobile Robotics (2000) — 320 citations