Minija Tamosiunaite

Reinforcement Learning · 2009–2012

A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 24 citations into it
• Control and Systems Engineering — 19 citations into it; #3,503 of 8,257 by trust here, #4,045 by citations
• Biomedical Engineering — 16 citations into it; #4,848 of 10,734 by trust here, #9,032 by citations
• Artificial Intelligence — 15 citations into it; #17,810 of 35,345 by trust here, #23,077 by citations
• Robotics & Control — 11 citations into it
• Cognitive Neuroscience — 5 citations into it
• Social Psychology — 5 citations into it

Whom Minija Tamosiunaite trusts overall

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

1. Stefan Schaal
2. Auke Jan Ijspeert
3. Richard S. Sutton
4. Jun Nakanishi
5. Jan Peters
6. Evangelos A. Theodorou
7. Andrew Barto
8. Bojan Nemec
9. Christopher G. Atkeson
10. J. Kolter

Who trusts Minija Tamosiunaite overall

1. Freek Stulp
2. Harish Chaandar Ravichandar
3. Olivier Sigaud
4. Connor Schenck
5. Seyed Mohammad Khansari-Zadeh
6. Ashwin P. Dani
7. Jens Kober
8. Bojan Nemec

Papers from Minija Tamosiunaite's perspective

Ordered by how strongly the researchers Minija Tamosiunaite trusts endorse each paper. Same corpus, a different order for every researcher.

1. Reinforcement Learning: An Introduction
2. ImageNet classification with deep convolutional neural networks
3. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
4. Gradient-based learning applied to document recognition
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Long Short-Term Memory
7. Locally Weighted Learning
8. GENERATIVE ADVERSARIAL NETS
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Learning and generalization of motor skills by learning from demonstration

Their papers

• Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives (2011) — 113 citations
• Task adaptation through exploration and action sequencing (2009) — 29 citations
• Applying statistical generalization to determine search direction for reinforcement learning of movement primitives (2012) — 10 citations