Lukas Prantl

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 289 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #16,614 of 21,575 by trust here, #20,271 by citations
• Computational Mechanics — 57 citations into it; #6,290 of 10,001 by trust here, #6,877 by citations
• Artificial Intelligence — 47 citations into it
• Computer Graphics and Computer-Aided Design — 30 citations into it; #1,202 of 1,584 by trust here, #1,447 by citations
• Statistical and Nonlinear Physics — 29 citations into it; #8,912 of 10,127 by trust here, #8,120 by citations
• Generative Models — 19 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Environmental Engineering — 7 citations into it

Whom Lukas Prantl trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Ross B. Girshick
5. Radford M. Neal
6. Yoshua Bengio
7. Yann LeCun
8. Kaiming He
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Lukas Prantl overall

1. Shuhao Cao
2. Steffen Wiewel
3. L. Zampieri
4. Nils Thürey
5. Tobias Pfaff
6. Philipp Holl
7. Alvaro Sanchez-Gonzalez
8. Pingchuan Ma

Papers from Lukas Prantl's perspective

Ordered by how strongly the researchers Lukas Prantl 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Lagrangian Fluid Simulation with Continuous Convolutions (2020) — 218 citations
• Well, how accurate is it? A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations (2018) — 22 citations
• Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows (2018) — 532 citations
• Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow (2017) — 15 citations
• Generating Liquid Simulations with Deformation-aware Neural Networks (2017) — 16 citations