Arunkumar Byravan

Computer Vision and Pattern Recognition · 2014–2025

A researcher in the Science corpus: 6 papers; trusts 417 researchers and is trusted by 272 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it; #2,398 of 21,575 by trust here, #5,843 by citations
• Artificial Intelligence — 51 citations into it; #14,271 of 35,345 by trust here, #17,776 by citations
• Reinforcement Learning — 42 citations into it; #968 of 2,242 by trust here, #1,280 by citations
• Control and Systems Engineering — 31 citations into it; #526 of 8,257 by trust here, #1,064 by citations
• Aerospace Engineering — 20 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Robotics & Control — 12 citations into it
• Model Architectures — 5 citations into it

Whom Arunkumar Byravan trusts overall

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

1. Chelsea Finn
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Sergey Levine
5. Alex Krizhevsky
6. Yoshua Bengio
7. Kaiming He
8. Yann LeCun
9. Byron Boots
10. Pulkit Agrawal

Who trusts Arunkumar Byravan overall

1. Chelsea Finn
2. Sergey Levine
3. Frederik D. Ebert
4. Dieter Fox
5. Sudeep Dasari
6. Oliver Kroemer
7. Annie Xie
8. Suraj Nair
9. Chris Paxton
10. Clément Godard

Papers from Arunkumar Byravan's perspective

Ordered by how strongly the researchers Arunkumar Byravan 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• SE3-nets: Learning rigid body motion using deep neural networks (2016) — 281 citations
• SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control (2017) — 43 citations
• Learning predictive models of a depth camera & manipulator from raw execution traces (2014) — 44 citations
• Prospection: Interpretable plans from language by predicting the future (2019) — 51 citations
• SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Control (2018) — 37 citations
• Gemini Robotics: Bringing AI into the Physical World (2025) — 385 citations