Yunzhu Li

Artificial Intelligence · with Torralba · 2017–2023

A researcher in the Science corpus: 8 papers; trusts 523 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 213 citations into it; #12,337 of 35,345 by trust here, #8,007 by citations
• Computer Vision and Pattern Recognition — 192 citations into it; #4,772 of 21,575 by trust here, #4,690 by citations
• Reinforcement Learning — 112 citations into it; #770 of 2,242 by trust here, #1,084 by citations
• Control and Systems Engineering — 56 citations into it; #5,253 of 8,257 by trust here, #1,769 by citations
• Model Architectures — 26 citations into it
• Optimization & Training — 22 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Robotics & Control — 19 citations into it

Whom Yunzhu Li trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Ronald J. Williams
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Jimmy Ba
7. Alex Krizhevsky
8. Sergey Levine
9. Ilya Sutskever
10. John Schulman

Who trusts Yunzhu Li overall

1. Lei Tai
2. Abhishek Gupta
3. Sergey Levine
4. Nathan Gavenski
5. Pieter Abbeel
6. Jingwei Zhang
7. Jiaming Song
8. Shengjia Zhao
9. Adam Gleave
10. Stefano Ermon

Papers from Yunzhu Li's perspective

Ordered by how strongly the researchers Yunzhu Li 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• CLEVRER: CoLlision Events for Video REpresentation and Reasoning (2019) — 645 citations
• InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations (2017) — 404 citations
• Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs (2017) — 44 citations
• Visual Grounding of Learned Physical Models (2020) — 90 citations
• Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids (2018) — 479 citations
• Learning the signatures of the human grasp using a scalable tactile glove (2019) — 1,066 citations
• Propagation Networks for Model-Based Control Under Partial Observation (2018) — 158 citations
• VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models (2023) — 1,011 citations