Justin Yu

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it
• Reinforcement Learning — 34 citations into it
• Control and Systems Engineering — 26 citations into it
• Robotics & Control — 12 citations into it

Whom Justin Yu trusts overall

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

1. Sergey Levine
2. Pieter Abbeel
3. John Schulman
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Yoshua Bengio
7. J. Urgen Schmidhuber
8. Chelsea Finn
9. Richard S. Sutton
10. Alex Krizhevsky

Papers from Justin Yu's perspective

Ordered by how strongly the researchers Justin Yu 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• The Ingredients of Real-World Robotic Reinforcement Learning (2020) — 207 citations