Julian Gao

Reinforcement Learning · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 41 citations into it
• Artificial Intelligence — 38 citations into it

Whom Julian Gao trusts overall

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

1. Pieter Abbeel
2. Sergey Levine

Who trusts Julian Gao overall

1. Ajay Mandlekar
2. Tanmay Shankar

Papers from Julian Gao's perspective

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

1. ImageNet: A large-scale hierarchical image database
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Microsoft COCO: Common Objects in Context
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Proximal Policy Optimization Algorithms (arXiv.org)
10. MuJoCo: A physics engine for model-based control

Their papers

• Neural Task Programming: Learning to Generalize Across Hierarchical Tasks (2017) — 212 citations
• ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation (2018) — 387 citations