Ian Lenz

Computer Vision and Pattern Recognition · 2013–2015

A researcher in the Science corpus: 2 papers; trusts 100 researchers and is trusted by 243 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 32 citations into it; #2,644 of 21,575 by trust here, #4,955 by citations
• Control and Systems Engineering — 17 citations into it; #130 of 8,257 by trust here, #196 by citations
• Artificial Intelligence — 16 citations into it; #4,338 of 35,345 by trust here, #9,275 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Aerospace Engineering — 12 citations into it
• Robotics & Control — 9 citations into it
• Biomedical Engineering — 8 citations into it
• Natural Language Processing — 8 citations into it

Whom Ian Lenz trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Andrew Y. Ng
4. Yann LeCun
5. Quoc V. Le
6. Alex Krizhevsky
7. Ian J. Goodfellow
8. Christian Szegedy
9. Ilya Sutskever
10. Ross B. Girshick

Who trusts Ian Lenz overall

1. Sergey Levine
2. Lei Tai
3. Pieter Abbeel
4. Chelsea Finn
5. Jeffrey Mahler
6. Abhinav Kumar Gupta
7. Andy Zeng
8. Jason Aaron Edward Weston
9. Lerrel Pinto
10. Sulabh Kumra

Papers from Ian Lenz's perspective

Ordered by how strongly the researchers Ian Lenz 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. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Deep learning for detecting robotic grasps (2013) — 1,772 citations
• DeepMPC: Learning Deep Latent Features for Model Predictive Control (2015) — 377 citations