Sriram Srinivasan

Reinforcement Learning · with Bowling · 2014–2019

A researcher in the Science corpus: 6 papers; trusts 47 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 137 citations into it; #562 of 2,242 by trust here, #299 by citations
• Artificial Intelligence — 133 citations into it; #4,810 of 35,345 by trust here, #3,192 by citations
• Game & Decision Theory — 72 citations into it
• Management Science and Operations Research — 62 citations into it
• Economics and Econometrics — 30 citations into it; #5,592 of 23,481 by trust here, #1,173 by citations
• Statistical and Nonlinear Physics — 21 citations into it
• Optimization & Training — 16 citations into it
• Computer Networks and Communications — 10 citations into it

Whom Sriram Srinivasan trusts overall

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

1. Michael H. Bowling
2. Richard S. Sutton
3. Martin A. Zinkevich
4. Ronald J. Williams
5. Diederik P. Kingma
6. Andrew Barto
7. Marc G. Bellemare
8. Volodymyr Mnih
9. Mark Newman
10. Geoffrey E. Hinton

Who trusts Sriram Srinivasan overall

1. Yuxi Li
2. Sergey Levine
3. Marc G. Bellemare
4. Brendan O'Donoghue
5. Ian Osband
6. Marlos C. Machado
7. Pieter Abbeel
8. Romain Laroche
9. Yan Zhang
10. Yoshua Bengio

Papers from Sriram Srinivasan's perspective

Ordered by how strongly the researchers Sriram 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. 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. Reinforcement Learning: An Introduction
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Unifying Count-Based Exploration and Intrinsic Motivation (2016) — 1,688 citations
• Learning human behaviors from motion capture by adversarial imitation (2017) — 221 citations
• Domain-Independent Optimistic Initialization for Reinforcement Learning (2014) — 27 citations
• Actor-Critic Policy Optimization in Partially Observable Multiagent Environments (2018) — 159 citations
• OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations
• On the permanence of vertices in network communities (2014) — 138 citations