Edoardo Conti

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 88 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it; #10,598 of 35,345 by trust here, #6,266 by citations
• Reinforcement Learning — 113 citations into it; #1,263 of 2,242 by trust here, #751 by citations
• Optimization & Training — 39 citations into it
• Management Science and Operations Research — 29 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Computational Theory and Mathematics — 14 citations into it; #8,200 of 12,182 by trust here, #4,696 by citations
• Evaluation & Benchmarks — 11 citations into it; #2,161 of 3,147 by trust here, #2,369 by citations
• Control and Systems Engineering — 9 citations into it

Whom Edoardo Conti trusts overall

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

1. Richard S. Sutton
2. Marc G. Bellemare
3. Diederik P. Kingma
4. J. Urgen Schmidhuber
5. Geoffrey E. Hinton
6. Ronald J. Williams
7. Volodymyr Mnih
8. Alex Graves
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Edoardo Conti overall

1. Jeff Clune
2. Scott Fujimoto
3. Shauharda Khadka
4. Steven Dalton
5. Felipe Petroski Such
6. Jiale Zhi
7. Christopher Stanton
8. Rui Wang
9. Yunbo Zhang
10. Kenneth O. Stanley

Papers from Edoardo Conti's perspective

Ordered by how strongly the researchers Edoardo Conti 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Benchmarking Batch Deep Reinforcement Learning Algorithms (2019) — 218 citations
• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning (2017) — 759 citations
• Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents (2017) — 382 citations
• Horizon: Facebook's Open Source Applied Reinforcement Learning Platform (2018) — 147 citations