Adrian Šošić

Reinforcement Learning · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 76 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 48 citations into it; #1,254 of 2,242 by trust here, #1,728 by citations
• Artificial Intelligence — 39 citations into it; #18,910 of 35,345 by trust here, #27,551 by citations
• Computer Networks and Communications — 22 citations into it; #4,695 of 9,886 by trust here, #7,140 by citations
• Computational Theory and Mathematics — 12 citations into it; #7,142 of 12,182 by trust here, #8,294 by citations
• Control and Systems Engineering — 12 citations into it
• Mechanical Engineering — 12 citations into it; #7,037 of 8,300 by trust here, #8,205 by citations
• Computer Vision and Pattern Recognition — 9 citations into it
• Management Science and Operations Research — 9 citations into it

Whom Adrian Šošić trusts overall

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

1. Andrew Y. Ng
2. Pieter Abbeel
3. Richard S. Sutton
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Christopher J. C. H. Watkins
7. John Schulman
8. Frans Adriaan Oliehoek
9. Matthew J. Hausknecht
10. Yoshua Bengio

Who trusts Adrian Šošić overall

1. Maximilian Hüttenrauch
2. Gerhard Neumann
3. Tabish Rashid
4. Albert Tung
5. Thanh Thi Nguyen
6. Anuj Mahajan
7. Mikayel Samvelyan

Papers from Adrian Šošić's perspective

Ordered by how strongly the researchers Adrian Šošić 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Reinforcement Learning: An Introduction
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning (2017) — 32 citations
• Guided Deep Reinforcement Learning for Swarm Systems (2017) — 149 citations
• Deep Reinforcement Learning for Swarm Systems (2018) — 250 citations
• Inverse Reinforcement Learning in Swarm Systems (2016) — 82 citations