Marc Rußwurm

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 171 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Media Technology — 23 citations into it; #1,106 of 3,621 by trust here, #2,598 by citations
• Model Architectures — 23 citations into it
• Ecology — 16 citations into it; #2,135 of 10,717 by trust here, #8,686 by citations
• Signal Processing — 15 citations into it; #5,339 of 7,572 by trust here, #6,836 by citations
• Natural Language Processing — 11 citations into it
• Atmospheric Science — 10 citations into it

Whom Marc Rußwurm trusts overall

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

1. Ilya Sutskever
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Yann LeCun
6. Sepp Hochreiter
7. Alex Graves
8. Gong Cheng
9. Alex Krizhevsky
10. Paul J. Werbos

Who trusts Marc Rußwurm overall

1. Yuan Yuan
2. Lei Lin
3. Lichao Mou
4. Charlotte Pelletier
5. Sabeen Ahmed
6. Yezhen Cong
7. Lorenzo Bruzzone
8. Geoffrey I. Webb
9. Ian E. Nielsen
10. Xiaoxiang Zhu

Papers from Marc Rußwurm's perspective

Ordered by how strongly the researchers Marc Rußwurm 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Tslearn, A Machine Learning Toolkit for Time Series Data (2020) — 534 citations
• Self-Attention for Raw Optical Satellite Time Series Classification (2019) — 345 citations
• Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders (2018) — 339 citations