Harsh Satija

Reinforcement Learning · 2018

A researcher in the Science corpus: 2 papers; trusts 91 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 64 citations into it
• Artificial Intelligence — 54 citations into it; #13,271 of 35,345 by trust here, #18,586 by citations
• Management Science and Operations Research — 15 citations into it; #3,914 of 11,946 by trust here, #5,548 by citations
• Probabilistic & Bayesian Methods — 12 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Control and Systems Engineering — 9 citations into it
• Optimization & Training — 7 citations into it
• Generative Models — 6 citations into it

Whom Harsh Satija trusts overall

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

1. Richard S. Sutton
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Ronald J. Williams
5. Yoshua Bengio
6. Alex Krizhevsky
7. Max Welling
8. Peter S. Dayan
9. John Schulman
10. Danilo Jimenez Rezende

Who trusts Harsh Satija overall

1. Yilun Du
2. Timothée Lesort
3. Dibya Ghosh
4. Georgios Papoudakis
5. Roberta Raileanu
6. Zhuangdi Zhu
7. Scott Fujimoto
8. Denis Yarats
9. Antonin Raffin

Papers from Harsh Satija's perspective

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

Their papers

• Decoupling Dynamics and Reward for Transfer Learning (2018) — 80 citations
• Randomized Value Functions via Multiplicative Normalizing Flows (2018) — 41 citations