Rahul Ramesh

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 2 papers; trusts 293 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #18,582 of 35,345 by trust here, #26,682 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Reinforcement Learning — 27 citations into it
• Meta & Continual Learning — 26 citations into it; #568 of 1,041 by trust here, #963 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• Model Architectures — 7 citations into it
• Management Science and Operations Research — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Rahul Ramesh trusts overall

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

1. Geoffrey E. Hinton
2. Arslan Chaudhry
3. Alex Krizhevsky
4. Yoshua Bengio
5. Marc'Aurelio Ranzato
6. Diederik P. Kingma
7. Yann LeCun
8. Kaiming He
9. Oriol Vinyals
10. Leo Breiman

Who trusts Rahul Ramesh overall

1. Jorge Armando Mendez Mendez
2. Liangzu Peng
3. Eric Eaton
4. Paris V. Giampouras
5. Liyuan Wang
6. René Vidal

Papers from Rahul Ramesh's perspective

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

Their papers

• Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning (2017) — 37 citations
• Model Zoo: A Growing Brain That Learns Continually (2021) — 82 citations