Tucker Hermans

Artificial Intelligence · 2011–2022

A researcher in the Science corpus: 4 papers; trusts 57 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #19,604 of 35,345 by trust here, #16,166 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #18,280 of 21,575 by trust here, #18,266 by citations
• Control and Systems Engineering — 29 citations into it; #4,335 of 8,257 by trust here, #2,213 by citations
• Reinforcement Learning — 27 citations into it
• Biomedical Engineering — 10 citations into it; #6,448 of 10,734 by trust here, #6,166 by citations
• Robotics & Control — 8 citations into it; #391 of 609 by trust here, #418 by citations

Whom Tucker Hermans trusts overall

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

1. Matthew Thomas Mason
2. Thomas K. Leung
3. David G. Lowe
4. Jan Peters
5. John Hartigan
6. Ivan Laptev
7. Christoph H. Lampert
8. Jitendra Malik
9. Ali Farhadi
10. Geoffrey E. Hinton

Who trusts Tucker Hermans overall

1. Tushar Nagarajan
2. Hema Swetha Koppula
3. Sergey Levine
4. Chelsea Finn
5. P. Hanrahan
6. Christoph Feichtenhofer
7. Aravind Rajeswaran
8. Vikash Kumar
9. Herke Van Hoof
10. Abhishek Gupta

Papers from Tucker Hermans's perspective

Ordered by how strongly the researchers Tucker Hermans 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Push planning for object placement on cluttered table surfaces (2011) — 133 citations
• Learning robot in-hand manipulation with tactile features (2015) — 180 citations
• Correcting Robot Plans with Natural Language Feedback (2022) — 128 citations
• Affordance Prediction via Learned Object Attributes (2011)