Gabriel Eilertsen

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 158 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 47 citations into it
• Artificial Intelligence — 41 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Safety, Robustness & Interpretability — 13 citations into it
• Model Architectures — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Gabriel Eilertsen trusts overall

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

1. Kaiming He
2. Christian Szegedy
3. Karen Simonyan
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Sergey Ioffe
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Yann LeCun
10. Andrew Zisserman

Who trusts Gabriel Eilertsen overall

1. Yuki Endo
2. Wencheng Wang
3. Demetris Marnerides
4. Shangzhe Wu
5. Ke Xu
6. Qingsen Yan
7. Yoshihiro Kanamori
8. Xiaojin Wu
9. Thomas Bashford-rogers

Papers from Gabriel Eilertsen's perspective

Ordered by how strongly the researchers Gabriel Eilertsen 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. 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. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• HDR image reconstruction from a single exposure using deep CNNs (2017) — 626 citations
• Measuring Domain Shift for Deep Learning in Histopathology (2020) — 282 citations