Max Bucher

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 1,102 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 166 citations into it; #4,988 of 21,575 by trust here, #4,988 by citations
• Artificial Intelligence — 135 citations into it; #6,038 of 35,345 by trust here, #5,749 by citations
• Representation & Self-Supervised Learning — 41 citations into it; #1,719 of 3,145 by trust here, #1,460 by citations
• Radiology, Nuclear Medicine and Imaging — 29 citations into it; #516 of 3,265 by trust here, #648 by citations
• Evaluation & Benchmarks — 19 citations into it
• Multimodal Learning — 17 citations into it
• Safety, Robustness & Interpretability — 17 citations into it
• Generative Models — 16 citations into it

Whom Max Bucher trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Kaiming He
6. Yoshua Bengio
7. Ilya Sutskever
8. Yann LeCun
9. Andrew Zisserman
10. David G. Lowe

Who trusts Max Bucher overall

1. Vinay Kumar Verma
2. Chong Zhou
3. Yongqin Xian
4. Golnaz Ghiasi
5. Mert Bulent Sariyildiz
6. Ping Hu
7. Li Zhang
8. Tuan-Hung Vu
9. Chen Change Loy
10. Piyush Rai

Papers from Max Bucher's perspective

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

• Zero-Shot Semantic Segmentation (2019) — 406 citations
• Generating Visual Representations for Zero-Shot Classification (2017) — 158 citations
• ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation (2018) — 1,529 citations
• Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classiffication (2016) — 220 citations
• Semantic bottleneck for computer vision tasks (2018) — 18 citations