Audrunas Gruslys

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 10 papers; trusts 91 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 228 citations into it; #10,237 of 35,345 by trust here, #2,747 by citations
• Reinforcement Learning — 226 citations into it; #625 of 2,242 by trust here, #221 by citations
• Management Science and Operations Research — 79 citations into it
• Game & Decision Theory — 53 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it; #15,782 of 21,575 by trust here, #10,905 by citations
• Cognitive Neuroscience — 28 citations into it
• Economics and Econometrics — 26 citations into it; #23,159 of 23,481 by trust here, #2,120 by citations
• Electrical and Electronic Engineering — 22 citations into it

Whom Audrunas Gruslys trusts overall

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

1. Volodymyr Mnih
2. Richard S. Sutton
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Graves
6. David Silver
7. Paul J. Werbos
8. Alex Krizhevsky
9. Tom Schaul
10. Marc G. Bellemare

Who trusts Audrunas Gruslys overall

1. Georg Ostrovski
2. Iasonas Kokkinos
3. Han Cai
4. Shansi Zhang
5. Marc G. Bellemare
6. Ofir Nachum
7. Danijar Hafner
8. Yuxi Li
9. Tal Ben-Nun
10. Kaiqing Zhang

Papers from Audrunas Gruslys's perspective

Ordered by how strongly the researchers Audrunas Gruslys 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. Long Short-Term Memory
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

• Deep Q-learning From Demonstrations (2017) — 1,289 citations
• Learning from Demonstrations for Real World Reinforcement Learning (2017) — 160 citations
• Memory-Efficient Backpropagation Through Time (2016) — 273 citations
• The Reactor: A Sample-Efficient Actor-Critic Architecture (2017) — 59 citations
• A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning (2017) — 750 citations
• Neural Replicator Dynamics (2019) — 25 citations
• Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents (2018) — 82 citations
• Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward (2018) — 895 citations
• Value-Decomposition Networks For Cooperative Multi-Agent Learning (2017) — 1,350 citations
• The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning (2017) — 103 citations