Kristian Hartikainen

Reinforcement Learning · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 102 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 136 citations into it; #769 of 2,242 by trust here, #473 by citations
• Artificial Intelligence — 127 citations into it; #7,339 of 35,345 by trust here, #4,722 by citations
• Control and Systems Engineering — 61 citations into it; #4,790 of 8,257 by trust here, #2,078 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Robotics & Control — 24 citations into it; #352 of 609 by trust here, #384 by citations
• Biomedical Engineering — 17 citations into it
• Management Science and Operations Research — 13 citations into it
• Optimization & Training — 13 citations into it

Whom Kristian Hartikainen trusts overall

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

1. Sergey Levine
2. John Schulman
3. Richard S. Sutton
4. Pieter Abbeel
5. E. Todorov
6. Tuomas Haarnoja
7. Geoffrey E. Hinton
8. Diederik P. Kingma
9. Ronald J. Williams
10. Brian D. Ziebart

Who trusts Kristian Hartikainen overall

1. Sergey Levine
2. Ilya Kostrikov
3. Avi Singh
4. Pieter Abbeel
5. Fei Xia
6. Chelsea Finn
7. Denis Yarats
8. Corey Lynch
9. Rob Fergus
10. Eugene Belilovsky

Papers from Kristian Hartikainen's perspective

Ordered by how strongly the researchers Kristian Hartikainen 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Soft Actor-Critic Algorithms and Applications (2018) — 3,200 citations
• Latent Space Policies for Hierarchical Reinforcement Learning (2018) — 208 citations
• End-to-End Robotic Reinforcement Learning without Reward Engineering (2019) — 289 citations
• The Ingredients of Real-World Robotic Reinforcement Learning (2020) — 207 citations