Michael Wand

Computer Vision and Pattern Recognition · with Li · 2013–2016

A researcher in the Science corpus: 3 papers; trusts 431 researchers and is trusted by 1,212 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it; #1,208 of 21,575 by trust here, #3,191 by citations
• Generative Models — 28 citations into it; #123 of 1,956 by trust here, #383 by citations
• Computer Graphics and Computer-Aided Design — 26 citations into it; #24 of 1,584 by trust here, #120 by citations
• Computational Mechanics — 21 citations into it
• Artificial Intelligence — 12 citations into it
• Safety, Robustness & Interpretability — 7 citations into it
• Media Technology — 5 citations into it

Whom Michael Wand trusts overall

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

1. Karen Simonyan
2. Alexei A. Efros
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Andrew Zisserman
7. Kaiming He
8. Matthew D. Zeiler
9. Vivek Kwatra
10. Leon A. Gatys

Who trusts Michael Wand overall

1. Yijun Li
2. Dae Young Park
3. Dongdong Chen
4. Yongcheng Jing
5. Xun Huang
6. Roman Novak
7. Jun-Yan Zhu
8. Yaroslav Nikulin
9. Samaneh Azadi
10. Amir Atapour-Abarghouei

Papers from Michael Wand's perspective

Ordered by how strongly the researchers Michael Wand 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks (2016) — 1,522 citations
• Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis (2016) — 811 citations
• Symmetry in 3D Geometry: Extraction and Applications (2013) — 387 citations