Andrew Hundt

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 222 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 63 citations into it; #17,048 of 21,575 by trust here, #17,258 by citations
• Artificial Intelligence — 58 citations into it; #27,876 of 35,345 by trust here, #26,682 by citations
• Control and Systems Engineering — 44 citations into it
• Reinforcement Learning — 29 citations into it
• Model Architectures — 19 citations into it
• Robotics & Control — 16 citations into it
• Human-Computer Interaction — 13 citations into it
• Optimization & Training — 12 citations into it

Whom Andrew Hundt trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Sergey Levine
5. Christian Szegedy
6. Yoshua Bengio
7. Karen Simonyan
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Andrew Hundt overall

1. Niv Nayman
2. Asaf Noy
3. Xin He
4. Chaoyang He
5. Dimitrios Stamoulis
6. Kaiyong Zhao
7. Tal Ridnik
8. Haishan Ye
9. Ruizhou Ding

Papers from Andrew Hundt's perspective

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

Their papers

• sharpDARTS: Faster and More Accurate Differentiable Architecture Search (2019) — 71 citations
• “Good Robot!”: Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer (2019) — 74 citations
• The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints (2018) — 14 citations