Lorenzo Steccanella

Reinforcement Learning · 2020

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 17 citations into it
• Artificial Intelligence — 11 citations into it

Whom Lorenzo Steccanella trusts overall

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

1. Richard S. Sutton
2. Martin L. Puterman
3. Tom Schaul
4. Alexander L. Strehl
5. Geoffrey E. Hinton
6. Ofir Nachum
7. Junhyuk Oh
8. Michael L. Littman
9. Satinder Singh
10. Wenling Shang

Papers from Lorenzo Steccanella's perspective

Ordered by how strongly the researchers Lorenzo Steccanella 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Hierarchical reinforcement learning for efficient exploration and transfer (2020) — 10 citations