Markus H. Gross

Artificial Intelligence · with Ancona · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 301 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #4,274 of 35,345 by trust here, #6,422 by citations
• Safety, Robustness & Interpretability — 70 citations into it; #343 of 2,251 by trust here, #593 by citations
• Computer Vision and Pattern Recognition — 57 citations into it
• Biophysics — 20 citations into it
• Natural Language Processing — 16 citations into it
• Model Architectures — 9 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Economics and Econometrics — 5 citations into it

Whom Markus H. Gross trusts overall

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

1. Matthew D. Zeiler
2. Alex Krizhevsky
3. Yann LeCun
4. Geoffrey E. Hinton
5. Avanti Shrikumar
6. Karen Simonyan
7. Grgoire Montavon
8. Christian Szegedy
9. Yoshua Bengio
10. Rob Fergus

Who trusts Markus H. Gross overall

1. Wojciech Samek
2. Reza Shokri
3. Hugh Chen
4. Laura Rieger
5. Scott M. Lundberg
6. Arun Das
7. Luke Merrick
8. Grgoire Montavon
9. Martin Strobel
10. Gabrielle Ras

Papers from Markus H. Gross's perspective

Ordered by how strongly the researchers Markus H. Gross 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet Large Scale Visual Recognition Challenge
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Attention is All you Need (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A unified view of gradient-based attribution methods for Deep Neural Networks (2017) — 149 citations
• Towards better understanding of gradient-based attribution methods for Deep Neural Networks (2017) — 1,034 citations
• Gradient-Based Attribution Methods (2019) — 191 citations
• Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation (2019) — 272 citations