Cédric Colas

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 8 papers; trusts 116 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 180 citations into it; #15,099 of 35,345 by trust here, #17,110 by citations
• Reinforcement Learning — 151 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it; #11,014 of 21,575 by trust here, #15,162 by citations
• Control and Systems Engineering — 30 citations into it
• Management Science and Operations Research — 21 citations into it
• Cognitive Neuroscience — 18 citations into it
• Computational Theory and Mathematics — 15 citations into it
• Developmental and Educational Psychology — 15 citations into it

Whom Cédric Colas trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Sergey Levine
4. Pieter Abbeel
5. John Schulman
6. J. Urgen Schmidhuber
7. Andrew Barto
8. Diederik P. Kingma
9. David Silver
10. Yoshua Bengio

Who trusts Cédric Colas overall

1. Sébastien Forestier
2. Vitchyr H. Pong
3. Yoan Mollard
4. Pierre-Yves Oudeyer
5. Lisa Lee
6. Shauharda Khadka
7. Kalifou René Traoré
8. Scott Fujimoto
9. Timothée Lesort
10. Adrien Ecoffet

Papers from Cédric Colas's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (2018) — 191 citations
• GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms (2017) — 169 citations
• When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settings (2024)
• When LLMs Play the Telephone Game: Cumulative Changes and Attractors in Iterated Cultural Transmissions (2024)
• Vygotskian Autotelic Artificial Intelligence: Language and Culture Internalization for Human-Like AI (2022) — 6 citations
• Grounding Language to Autonomously-Acquired Skills via Goal Generation (2021) — 73 citations
• Automatic Curriculum Learning For Deep RL: A Short Survey (2020) — 235 citations
• CURIOUS: Intrinsically Motivated Multi-Task, Multi-Goal Reinforcement Learning (2018) — 41 citations