Carlo D'Eramo

Reinforcement Learning · 2016–2022

A researcher in the Science corpus: 5 papers; trusts 36 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 63 citations into it
• Artificial Intelligence — 58 citations into it
• Management Science and Operations Research — 17 citations into it
• Control and Systems Engineering — 16 citations into it
• Statistics and Probability — 16 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Statistical Learning Theory — 10 citations into it
• Optimization & Training — 9 citations into it

Whom Carlo D'Eramo trusts overall

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

1. Hado van Hasselt
2. Richard S. Sutton
3. Damien Ernst
4. Csaba Szepesvari
5. Andreas Maurer
6. R´emi Munos
7. Martin A. Riedmiller
8. Jonathan Baxter
9. M. Stone
10. Michail G. Lagoudakis

Who trusts Carlo D'Eramo overall

1. Min He
2. Pingli Lv
3. Athul Paul Jacob
4. Hongliang Guo
5. Zhiyuan Xu
6. Takahisa Imagawa
7. Michael Crawshaw
8. Xuesong Wang
9. Alessandro Nuara
10. Emilie Kaufmann

Papers from Carlo D'Eramo's perspective

Ordered by how strongly the researchers Carlo D'Eramo 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. Reinforcement Learning: An Introduction
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
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

• Sharing Knowledge in Multi-Task Deep Reinforcement Learning (2020) — 156 citations
• Boosted Curriculum Reinforcement Learning (2022) — 11 citations
• Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems (2017) — 20 citations
• Boosted Fitted Q-Iteration (2017) — 49 citations
• Estimating Maximum Expected Value through Gaussian Approximation (2016) — 46 citations