Michal Mackiewicz

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 275 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 57 citations into it; #10,506 of 21,575 by trust here, #12,992 by citations
• Artificial Intelligence — 54 citations into it
• Representation & Self-Supervised Learning — 29 citations into it; #806 of 3,145 by trust here, #1,266 by citations
• Optimization & Training — 16 citations into it
• Model Architectures — 7 citations into it

Whom Michal Mackiewicz trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Christian Szegedy
6. Sergey Ioffe
7. Yoshua Bengio
8. Karen Simonyan
9. Yann LeCun
10. Ross B. Girshick

Who trusts Michal Mackiewicz overall

1. Geoff French
2. Xingchao Peng
3. Ben Athiwaratkun
4. Lukas Hoyer
5. Garrett Wilson
6. Yanbei Chen
7. Luke Melas-Kyriazi
8. Viktor Olsson
9. Jinhong Deng
10. Wilhelm Tranheden

Papers from Michal Mackiewicz's perspective

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

Their papers

• Semi-supervised semantic segmentation needs strong, varied perturbations (2019) — 378 citations
• Semi-supervised semantic segmentation needs strong, high-dimensional perturbations (2019) — 137 citations
• Self-ensembling for visual domain adaptation (2017) — 559 citations