Tianmin Shu

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 7 papers; trusts 408 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 228 citations into it; #21,542 of 35,345 by trust here, #9,369 by citations
• Computer Vision and Pattern Recognition — 122 citations into it; #19,815 of 21,575 by trust here, #9,435 by citations
• Reinforcement Learning — 108 citations into it; #1,867 of 2,242 by trust here, #1,415 by citations
• Natural Language Processing — 54 citations into it
• Control and Systems Engineering — 22 citations into it; #5,085 of 8,257 by trust here, #1,814 by citations
• Model Architectures — 20 citations into it
• LLM Agents & Tool Use — 13 citations into it
• Management Science and Operations Research — 12 citations into it

Whom Tianmin Shu trusts overall

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

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yann LeCun
7. Volodymyr Mnih
8. Kaiming He
9. Ilya Sutskever
10. Alex Graves

Who trusts Tianmin Shu overall

1. Shagun Sodhani
2. William H. Guss
3. Yiding Jiang
4. Xavier Puig
5. Arthur Juliani
6. Daniel Toyama
7. Matthew Riemer
8. Khimya Khetarpal
9. Amy Zhang
10. Caiming Xiong

Papers from Tianmin Shu's perspective

Ordered by how strongly the researchers Tianmin Shu 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. 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

• Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning (2017) — 150 citations
• VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning (2019) — 67 citations
• CERN: Confidence-Energy Recurrent Network for Group Activity Recognition (2017) — 162 citations
• RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning (2022) — 520 citations
• Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration (2020) — 185 citations
• Building Cooperative Embodied Agents Modularly with Large Language Models (2023) — 315 citations
• Interactive Agent Modeling by Learning to Probe (2018)