Benjamin Kellenberger

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 367 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 89 citations into it; #13,128 of 21,575 by trust here, #9,872 by citations
• Artificial Intelligence — 60 citations into it; #26,852 of 35,345 by trust here, #18,586 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Model Architectures — 21 citations into it
• Media Technology — 17 citations into it
• Optimization & Training — 9 citations into it
• Generative Models — 7 citations into it
• Environmental Engineering — 5 citations into it

Whom Benjamin Kellenberger trusts overall

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

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

Who trusts Benjamin Kellenberger overall

1. Mohammad Rostami
2. M. Nowak
3. David W. Romero
4. Osman Semih Kayhan
5. Serban Stan
6. Katarzyna Dziób
7. Erik J. Bekkers
8. Ivan Sosnovik
9. Chen-Yu Lee

Papers from Benjamin Kellenberger's perspective

Ordered by how strongly the researchers Benjamin Kellenberger 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation (2018) — 541 citations
• Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models (2018) — 261 citations
• Perspectives in machine learning for wildlife conservation (2021) — 659 citations
• Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning (2018) — 304 citations
• Scale equivariance in CNNs with vector fields (2018) — 59 citations