Shengkai Huang

Artificial Intelligence · 2021

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

• Artificial Intelligence — 33 citations into it
• Reinforcement Learning — 33 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Shengkai Huang trusts overall

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

1. Richard S. Sutton
2. Sergey Levine

Papers from Shengkai Huang's perspective

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

1. Reinforcement Learning: An Introduction
2. Human-level control through deep reinforcement learning
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. MuJoCo: A physics engine for model-based control
6. Playing Atari with Deep Reinforcement Learning (arXiv.org)
7. Proximal Policy Optimization Algorithms (arXiv.org)
8. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
9. Gradient-based learning applied to document recognition
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning (2021) — 99 citations