Rajat Agarwal

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 34 citations into it
• Artificial Intelligence — 33 citations into it
• Computational Theory and Mathematics — 8 citations into it

Whom Rajat Agarwal trusts overall

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

1. Richard S. Sutton
2. E. Todorov
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Danilo Jimenez Rezende
6. R´emi Munos
7. Yuval Tassa
8. Sergey Levine
9. Yoshua Bengio
10. Timothy P. Lillicrap

Papers from Rajat Agarwal's perspective

Ordered by how strongly the researchers Rajat Agarwal 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. Long Short-Term Memory
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Continuous control with deep reinforcement learning (International Conference on Learning Representations)

Their papers

• Dynamics-aware Embeddings (2019) — 59 citations