Miles Macklin

Control and Systems Engineering · 2013–2022

A researcher in the Science corpus: 8 papers; trusts 48 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 80 citations into it
• Artificial Intelligence — 70 citations into it; #34,955 of 35,345 by trust here, #11,072 by citations
• Computer Vision and Pattern Recognition — 61 citations into it; #12,303 of 21,575 by trust here, #8,831 by citations
• Reinforcement Learning — 50 citations into it
• Robotics & Control — 34 citations into it; #594 of 609 by trust here, #256 by citations
• Computational Mechanics — 32 citations into it; #1,797 of 10,001 by trust here, #3,029 by citations
• Computer Graphics and Computer-Aided Design — 28 citations into it; #288 of 1,584 by trust here, #605 by citations
• Biomedical Engineering — 24 citations into it

Whom Miles Macklin trusts overall

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

1. Joseph J Monaghan
2. Matthias Müller
3. Ronald Fedkiw
4. Barbara Solenthaler
5. Robert Bridson
6. Bruno Heidelberger
7. Eran Guendelman
8. Andrew Nealen
9. Nadir Akinci
10. Renato Pajarola

Who trusts Miles Macklin overall

1. Yunzhu Li
2. Benjamin Ummenhofer
3. Jan Bender
4. Arsalan Mousavian
5. Lukas Prantl
6. Nils Thürey
7. Yun Teng
8. Lubor Ladicky
9. Yuanming Hu
10. Jiajun Wu

Papers from Miles Macklin's perspective

Ordered by how strongly the researchers Miles Macklin 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. Gradient-based learning applied to document recognition
6. Et al
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Asynchronous Methods for Deep Reinforcement Learning
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning (2021) — 1,851 citations
• Unified particle physics for real-time applications (2014) — 550 citations
• Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience (2018) — 623 citations
• Position-Based Simulation Methods in Computer Graphics (2015) — 126 citations
• Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands (2022) — 106 citations
• GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning (2018) — 245 citations
• XPBD: position-based simulation of compliant constrained dynamics (2016) — 457 citations
• Position based fluids (2013) — 500 citations