Mateusz Michalkiewicz

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 2 papers; trusts 170 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 55 citations into it
• Computational Mechanics — 22 citations into it; #7,582 of 10,001 by trust here, #2,574 by citations
• 3D Vision & Pose — 16 citations into it; #562 of 689 by trust here, #380 by citations
• Computer Graphics and Computer-Aided Design — 16 citations into it; #1,442 of 1,584 by trust here, #691 by citations
• Geology — 10 citations into it

Whom Mateusz Michalkiewicz trusts overall

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

1. Ross T. Whitaker
2. Geoffrey E. Hinton
3. Jia Deng
4. Alex Krizhevsky
5. Alexey Dosovitskiy
6. Stanley J. Osher
7. Kaiming He
8. William E. Lorensen
9. Diederik P. Kingma
10. Fakir S. Nooruddin

Who trusts Mateusz Michalkiewicz overall

1. Michael Niemeyer
2. Julian Chibane
3. Yinbo Chen
4. Matthew Tancik
5. Wenqi Xian
6. Peng Wang
7. Lars M. Mescheder
8. Amos Gropp
9. Lingjie Liu
10. Rohan Chabra

Papers from Mateusz Michalkiewicz's perspective

Ordered by how strongly the researchers Mateusz Michalkiewicz 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Deep Level Sets: Implicit Surface Representations for 3D Shape Inference (2019) — 131 citations
• Implicit Surface Representations As Layers in Neural Networks (2019) — 302 citations