Minghuan Liu

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 92 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it
• Computer Vision and Pattern Recognition — 62 citations into it
• Reinforcement Learning — 46 citations into it
• Control and Systems Engineering — 13 citations into it
• Generative Models — 13 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 7 citations into it

Whom Minghuan Liu trusts overall

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

1. Geoffrey E. Hinton
2. Pieter Abbeel
3. Yann LeCun
4. Sergey Levine
5. Andrew Y. Ng
6. Yoshua Bengio
7. Diederik P. Kingma
8. Pascal Vincent
9. Chelsea Finn
10. Alex Krizhevsky

Who trusts Minghuan Liu overall

1. Peter R. Florence

Papers from Minghuan Liu's perspective

Ordered by how strongly the researchers Minghuan Liu 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation (2023) — 380 citations
• Vision-Language Foundation Models as Effective Robot Imitators (2023) — 418 citations
• Energy-Based Imitation Learning (2020) — 55 citations