Anders P. Eriksson

Computer Vision and Pattern Recognition · 2012–2019

A researcher in the Science corpus: 5 papers; trusts 325 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 98 citations into it; #8,988 of 21,575 by trust here, #12,419 by citations
• Computational Mechanics — 56 citations into it; #2,983 of 10,001 by trust here, #1,985 by citations
• 3D Vision & Pose — 35 citations into it; #558 of 689 by trust here, #380 by citations
• Aerospace Engineering — 31 citations into it
• Computer Graphics and Computer-Aided Design — 24 citations into it; #1,438 of 1,584 by trust here, #691 by citations
• Geology — 19 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Optimization & Training — 9 citations into it

Whom Anders P. Eriksson trusts overall

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

1. Erkki Oja
2. Pierre Baldi
3. Manmohan Chandraker
4. Qifa Ke
5. E.J. Candes
6. Geoffrey E. Hinton
7. Diederik P. Kingma
8. Yann LeCun
9. Alex Krizhevsky
10. Takeo Kanade

Who trusts Anders P. Eriksson overall

1. Brendt E. Wohlberg
2. Matthew D. Zeiler
3. Dmitry Ulyanov
4. Vardan Papyan
5. Tran Minh Quan
6. Yu Liu
7. Shuhang Gu
8. Yuanzhi Li

Papers from Anders P. Eriksson's perspective

Ordered by how strongly the researchers Anders P. Eriksson trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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

• Deep Level Sets: Implicit Surface Representations for 3D Shape Inference (2019) — 131 citations
• Fast Convolutional Sparse Coding (2013) — 364 citations
• Implicit Surface Representations As Layers in Neural Networks (2019) — 302 citations
• Image2Mesh: A Learning Framework for Single Image 3D Reconstruction (2017) — 138 citations
• Efficient Computation of Robust Weighted Low-Rank Matrix Approximations Using the L_1 Norm (2012) — 67 citations