Rudhir Gupta

Computer Vision and Pattern Recognition · 2012

A researcher in the Science corpus: 2 papers; trusts 348 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it; #7,632 of 21,575 by trust here, #6,426 by citations
• Artificial Intelligence — 37 citations into it; #11,790 of 35,345 by trust here, #9,092 by citations
• Control and Systems Engineering — 17 citations into it; #2,080 of 8,257 by trust here, #837 by citations
• Aerospace Engineering — 16 citations into it
• Biomedical Engineering — 9 citations into it
• 3D Vision & Pose — 8 citations into it
• Robotics & Control — 5 citations into it; #162 of 609 by trust here, #174 by citations

Whom Rudhir Gupta trusts overall

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

1. Thorsten Joachims
2. Ashutosh Saxena
3. Thomas Finley
4. Pedro F. Felzenszwalb
5. Geoffrey E. Hinton
6. Daphne Koller
7. Alex Krizhevsky
8. Yun Jiang
9. Li Fei-Fei
10. David G. Lowe

Who trusts Rudhir Gupta overall

1. Hema Swetha Koppula
2. Ashutosh Saxena
3. Junjie Shan
4. Luiza Mici
5. Fei Han
6. Ashesh Jain
7. Dipendra Kumar Misra
8. Ziyun Cai
9. Jianfang Hu
10. Lukas Rybok

Papers from Rudhir Gupta's perspective

Ordered by how strongly the researchers Rudhir Gupta 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning human activities and object affordances from RGB-D videos (2012) — 756 citations
• Human Activity Learning using Object Affordances from RGB-D Videos (2012) — 19 citations