Sulabh Kumra

Control and Systems Engineering · 2016–2019

A researcher in the Science corpus: 2 papers; trusts 156 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 33 citations into it; #1,456 of 8,257 by trust here, #5,490 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #6,842 of 21,575 by trust here, #18,266 by citations
• Artificial Intelligence — 16 citations into it
• Biomedical Engineering — 15 citations into it
• Human-Computer Interaction — 12 citations into it; #340 of 2,253 by trust here, #1,743 by citations
• Robotics & Control — 10 citations into it
• Aerospace Engineering — 9 citations into it
• Reinforcement Learning — 6 citations into it

Whom Sulabh Kumra trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Kaiming He
4. Lerrel Pinto
5. Joseph Redmon
6. Geoffrey E. Hinton
7. Sergey Levine
8. Edward Johns
9. Ilya Sutskever
10. Yoshua Bengio

Who trusts Sulabh Kumra overall

1. Douglas Morrison
2. Andrew Hundt
3. Guoguang Du
4. Peter Corke
5. Fu-Jen Chu
6. Jürgen Leitner
7. Ruinian Xu
8. Kai Wang
9. Shirin Joshi
10. Varun Jain

Papers from Sulabh Kumra's perspective

Ordered by how strongly the researchers Sulabh Kumra 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. 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

• Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network (2019) — 399 citations
• Robotic grasp detection using deep convolutional neural networks (2016) — 471 citations