Ravi Srinivasan

Artificial Intelligence · with Brown · 2020

A researcher in the Science corpus: 1 paper; trusts 28 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #27,520 of 35,345 by trust here, #27,551 by citations
• Reinforcement Learning — 36 citations into it; #1,864 of 2,242 by trust here, #1,999 by citations
• Control and Systems Engineering — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it; #16,931 of 21,575 by trust here, #17,757 by citations
• Management Science and Operations Research — 7 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Optimization & Training — 6 citations into it
• Safety, Robustness & Interpretability — 5 citations into it

Whom Ravi Srinivasan trusts overall

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

1. Diederik P. Kingma
2. David John Cameron MacKay
3. Geoffrey E. Hinton
4. Richard S. Sutton
5. Yarin Gal
6. Alex Krizhevsky
7. Yoshua Bengio
8. Dean A. Pomerleau
9. Pieter Abbeel
10. Radford M. Neal

Who trusts Ravi Srinivasan overall

1. Daniel S. Brown

Papers from Ravi Srinivasan's perspective

Ordered by how strongly the researchers Ravi Srinivasan trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences (2020) — 116 citations