Ludovic Righetti
Artificial Intelligence · 2006–2020
A researcher in the Science corpus: 9 papers; trusts 34 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 72 citations into it; #12,978 of 35,345 by trust here, #13,962 by citations
- Control and Systems Engineering — 70 citations into it; #1,065 of 8,257 by trust here, #792 by citations
- Reinforcement Learning — 65 citations into it; #1,648 of 2,242 by trust here, #1,663 by citations
- Robotics & Control — 34 citations into it; #217 of 609 by trust here, #431 by citations
- Computer Vision and Pattern Recognition — 32 citations into it
- Biomedical Engineering — 23 citations into it; #1,772 of 10,734 by trust here, #4,187 by citations
- Cognitive Neuroscience — 12 citations into it; #5,027 of 10,852 by trust here, #6,902 by citations
- Aerospace Engineering — 10 citations into it
Whom Ludovic Righetti trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- B. Kendall
- Evangelos A. Theodorou
- Jonas Buchli
- Stefan Schaal
- Andrew Y. Ng
- Mrinal Kalakrishnan
- Nathan D. Ratliff
- Jan Peters
- Ronald J. Williams
- Richard S. Sutton
Who trusts Ludovic Righetti overall
- Auke Jan Ijspeert
- Sergey Levine
- Tingfang Yan
- Seyed Mohammad Khansari-Zadeh
- Chelsea Finn
- Pieter Abbeel
- Aude Gemma Billard
- Abhishek Gupta
- Marco Cempini
- Péter Englert
Papers from Ludovic Righetti's perspective
Ordered by how strongly the researchers Ludovic Righetti trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Meta Learning via Learned Loss (2019) — 126 citations
- Learning objective functions for manipulation (2013) — 156 citations
- TriFinger: An Open-Source Robot for Learning Dexterity (2020) — 82 citations
- Learning force control policies for compliant manipulation (2011) — 176 citations
- Learning of grasp selection based on shape-templates (2013) — 134 citations
- Programmable central pattern generators: an application to biped locomotion control (2006) — 359 citations
- Learning motion primitive goals for robust manipulation (2011) — 49 citations
- On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward (2020) — 171 citations
- Dynamic hebbian learning in adaptive frequency oscillators (2006) — 401 citations