R. Sekar

Reinforcement Learning · with Abbeel · 2020

A researcher in the Science corpus: 1 paper; trusts 19 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 41 citations into it
• Artificial Intelligence — 37 citations into it

Whom R. Sekar trusts overall

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

1. Diederik P. Kingma
2. J. Urgen Schmidhuber
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. David John Cameron MacKay
6. Sergey Levine
7. Leo Breiman
8. Yoshua Bengio
9. Alex Krizhevsky
10. Leslie Pack Kaelbling

Who trusts R. Sekar overall

1. Danijar Hafner
2. Masashi Okada
3. Nicklas Hansen
4. Brandon Amos
5. Shibo Hao
6. Tadahiro Taniguchi
7. Xiaolong Wang

Papers from R. Sekar's perspective

Ordered by how strongly the researchers R. Sekar 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Planning to Explore via Self-Supervised World Models (2020) — 514 citations