Georgios Papoudakis

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 185 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 129 citations into it; #26,269 of 35,345 by trust here, #19,873 by citations
• Reinforcement Learning — 115 citations into it; #1,452 of 2,242 by trust here, #1,932 by citations
• Management Science and Operations Research — 29 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• LLM Agents & Tool Use — 15 citations into it
• Computational Theory and Mathematics — 12 citations into it; #9,156 of 12,182 by trust here, #5,954 by citations
• Computer Science Applications — 12 citations into it
• Game & Decision Theory — 12 citations into it

Whom Georgios Papoudakis trusts overall

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

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. Pieter Abbeel
4. Diederik P. Kingma
5. Stefano V. Albrecht
6. Volodymyr Mnih
7. Jakob N. Foerster
8. Yoshua Bengio
9. Ming Tan
10. Alex Krizhevsky

Who trusts Georgios Papoudakis overall

1. Filippos Christianos
2. Chao Yu
3. Stefano V. Albrecht
4. Akash Velu
5. Arrasy Rahman
6. Justin K. Terry
7. Eugene Vinitsky
8. Lukas Schäfer
9. Yu Wang
10. Niklas Höpner

Papers from Georgios Papoudakis's perspective

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

Their papers

• Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning (2019) — 244 citations
• Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing (2021) — 176 citations
• Local Information Agent Modelling in Partially-Observable Environments (2021)
• Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks (2020) — 367 citations
• Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms (2020) — 33 citations