Olivier Sigaud

Artificial Intelligence · 2006–2023

A researcher in the Science corpus: 18 papers; trusts 376 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 313 citations into it; #6,644 of 35,345 by trust here, #8,365 by citations
• Reinforcement Learning — 241 citations into it; #571 of 2,242 by trust here, #1,084 by citations
• Control and Systems Engineering — 107 citations into it; #1,776 of 8,257 by trust here, #1,213 by citations
• Computer Vision and Pattern Recognition — 104 citations into it; #9,232 of 21,575 by trust here, #12,992 by citations
• Cognitive Neuroscience — 60 citations into it
• Robotics & Control — 43 citations into it
• Representation & Self-Supervised Learning — 42 citations into it
• Natural Language Processing — 39 citations into it

Whom Olivier Sigaud trusts overall

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

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. Jan Peters
4. Stefan Schaal
5. Yoshua Bengio
6. Andrew Barto
7. Ronald J. Williams
8. Yann LeCun
9. Ilya Sutskever
10. J. Urgen Schmidhuber

Who trusts Olivier Sigaud overall

1. Alexander Rainer Tassilo Gepperth
2. J. Krichmar
3. Todd Hester
4. Sergey Levine
5. Marc Peter Deisenroth
6. Cem Karaoguz
7. Sébastien Forestier
8. Pierre-Yves Oudeyer
9. Mariano De Paula
10. Lukas Hewing

Papers from Olivier Sigaud's perspective

Ordered by how strongly the researchers Olivier Sigaud 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Compositional Neural Programs with Recursive Tree Search and Planning (2019) — 43 citations
• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (2018) — 191 citations
• Learning compact parameterized skills with a single regression (2013) — 90 citations
• Learning the structure of Factored Markov Decision Processes in reinforcement learning problems (2006) — 130 citations
• GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms (2017) — 169 citations
• Interactively shaping robot behaviour with unlabeled human instructions (2019) — 23 citations
• Tensor Based Knowledge Transfer Across Skill Categories for Robot Control (2017) — 25 citations
• Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning (2023) — 272 citations
• Towards Deep Developmental Learning (2016) — 47 citations
• The problem with DDPG: understanding failures in deterministic environments with sparse rewards (2019) — 73 citations
• DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics (2020) — 19 citations
• Grounding Language to Autonomously-Acquired Skills via Goal Generation (2021) — 73 citations
• CURIOUS: Intrinsically Motivated Multi-Task, Multi-Goal Reinforcement Learning (2018) — 41 citations
• Policy Improvement : Between Black-Box Optimization and Episodic Reinforcement Learning (2013)
• Path Integral Policy Improvement with Covariance Matrix Adaptation (2012) — 222 citations
• On-line regression algorithms for learning mechanical models of robots: A survey (2011) — 150 citations
• Learning Forward Models for the Operational Space Control of Redundant Robots (2010) — 22 citations
• From Motor Learning to Interaction Learning in Robots (2010) — 121 citations