Anthony Simeonov

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it
• Artificial Intelligence — 26 citations into it
• Reinforcement Learning — 23 citations into it
• Control and Systems Engineering — 21 citations into it
• Aerospace Engineering — 17 citations into it
• Computer Graphics and Computer-Aided Design — 16 citations into it
• Robotics & Control — 7 citations into it
• Generative Models — 5 citations into it

Whom Anthony Simeonov trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Sergey Levine
5. Yoshua Bengio
6. Peter R. Florence
7. Jimmy Ba
8. John Schulman
9. Yann LeCun
10. Kaiming He

Who trusts Anthony Simeonov overall

1. Matthew Tancik

Papers from Anthony Simeonov's perspective

Ordered by how strongly the researchers Anthony Simeonov 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation (2021) — 315 citations
• Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion Planners (2019) — 318 citations