Maxim Tatarchenko

Computer Vision and Pattern Recognition · 2014–2019

A researcher in the Science corpus: 6 papers; trusts 956 researchers and is trusted by 1,261 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 203 citations into it; #1,186 of 21,575 by trust here, #2,712 by citations
• Computational Mechanics — 70 citations into it; #310 of 10,001 by trust here, #332 by citations
• Artificial Intelligence — 51 citations into it; #17,935 of 35,345 by trust here, #19,873 by citations
• Aerospace Engineering — 47 citations into it
• 3D Vision & Pose — 39 citations into it; #56 of 689 by trust here, #109 by citations
• Generative Models — 36 citations into it; #350 of 1,956 by trust here, #512 by citations
• Computer Graphics and Computer-Aided Design — 31 citations into it; #71 of 1,584 by trust here, #145 by citations
• Representation & Self-Supervised Learning — 27 citations into it; #1,881 of 3,145 by trust here, #2,299 by citations

Whom Maxim Tatarchenko trusts overall

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

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

Who trusts Maxim Tatarchenko overall

1. Stephan R. Richter
2. Jiajun Wu
3. Jitendra Malik
4. Bo Yang
5. Kyle Genova
6. Georgia Gkioxari
7. Xian-Feng Han
8. Ishan Misra
9. Hiroharu Kato
10. Andreas Geiger

Papers from Maxim Tatarchenko's perspective

Ordered by how strongly the researchers Maxim Tatarchenko 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs (2017) — 784 citations
• Multi-view 3D Models from Single Images with a Convolutional Network (2015) — 388 citations
• Learning to Generate Chairs, Tables and Cars with Convolutional Networks (2014) — 267 citations
• What Do Single-View 3D Reconstruction Networks Learn? (2019) — 502 citations
• Semi-Supervised Semantic Segmentation With High- and Low-Level Consistency (2019) — 469 citations
• Tangent Convolutions for Dense Prediction in 3D (2018) — 612 citations