Georg Martius

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 299 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #10,788 of 35,345 by trust here, #13,962 by citations
• Computer Vision and Pattern Recognition — 54 citations into it; #10,063 of 21,575 by trust here, #14,151 by citations
• Optimization & Training — 29 citations into it; #1,436 of 2,601 by trust here, #2,215 by citations
• Model Architectures — 28 citations into it
• Generative Models — 22 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Reinforcement Learning — 18 citations into it
• Natural Language Processing — 13 citations into it

Whom Georg Martius trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Yann LeCun
5. Jimmy Ba
6. Robert Tibshirani
7. Alex Krizhevsky
8. Alex Graves
9. Alex Smola
10. Kaiming He

Who trusts Georg Martius overall

1. Zichao Long
2. Subham S. Sahoo
3. Nicolas Loizou
4. Yiping Lu
5. Sharan Vaswani
6. Robert Mansel Gower
7. Christoph H. Lampert
8. Bin Dong
9. Issam Hadj Laradji
10. Niklas Pfister

Papers from Georg Martius's perspective

Ordered by how strongly the researchers Georg Martius 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Variational Autoencoders Pursue PCA Directions (by Accident) (2018) — 176 citations
• Extrapolation and learning equations (2016) — 213 citations
• Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains (2021) — 28 citations
• Learning Equations for Extrapolation and Control (2018) — 306 citations
• Differentiation of Blackbox Combinatorial Solvers (2019) — 350 citations
• L4: Practical loss-based stepsize adaptation for deep learning (2018) — 68 citations