Michael Posa

Control and Systems Engineering · 2014–2020

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 14 citations into it; #2,307 of 8,257 by trust here, #2,782 by citations
• Artificial Intelligence — 8 citations into it
• Biomedical Engineering — 7 citations into it; #2,123 of 10,734 by trust here, #6,028 by citations
• Computer Vision and Pattern Recognition — 7 citations into it; #8,754 of 21,575 by trust here, #12,419 by citations
• Reinforcement Learning — 5 citations into it

Whom Michael Posa trusts overall

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

1. Vincent Acary
2. John T. Betts
3. Jerry E. Pratt
4. K. Wampler
5. Zoran Popovic
6. Igor Mordatch
7. Eric R. Westervelt
8. E. Todorov
9. David E. Stewart
10. Brandon Amos

Who trusts Michael Posa overall

1. Marc Toussaint
2. Igor Mordatch
3. Alberto Rodriguez
4. Nikhil Chavan Dafle
5. Francois Robert Hogan
6. Michael Janner
7. Jessy W. Grizzle
8. E. Todorov
9. Josh Tenenbaum
10. Kendall Lowrey

Papers from Michael Posa's perspective

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

Their papers

• A direct method for trajectory optimization of rigid bodies through contact (2014) — 738 citations
• ContactNets: Learning of Discontinuous Contact Dynamics with Smooth, Implicit Representations (2020) — 99 citations