Sebastian Bach

Computer Vision and Pattern Recognition · 2015–2016

A researcher in the Science corpus: 6 papers; trusts 351 researchers and is trusted by 72 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 102 citations into it; #3,525 of 21,575 by trust here, #1,755 by citations
• Artificial Intelligence — 66 citations into it; #3,072 of 35,345 by trust here, #2,322 by citations
• Safety, Robustness & Interpretability — 29 citations into it; #209 of 2,251 by trust here, #161 by citations
• Representation & Self-Supervised Learning — 16 citations into it; #1,329 of 3,145 by trust here, #1,913 by citations
• Evaluation & Benchmarks — 15 citations into it
• Biophysics — 11 citations into it; #72 of 1,196 by trust here, #48 by citations
• Model Architectures — 11 citations into it
• Cognitive Neuroscience — 8 citations into it; #2,828 of 10,852 by trust here, #3,802 by citations

Whom Sebastian Bach trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. David G. Lowe
5. Karen Simonyan
6. Andrew Zisserman
7. Matthew D. Zeiler
8. Ilya Sutskever
9. Jianguo Zhang
10. Yoshua Bengio

Who trusts Sebastian Bach overall

1. Wojciech Samek
2. Grgoire Montavon
3. K. Müller
4. Sebastian Lapuschkin
5. Alexander Binder
6. Giulia Vilone
7. Marco Ancona
8. Christopher J. Anders
9. Andreas Holzinger
10. Leila Arras

Papers from Sebastian Bach's perspective

Ordered by how strongly the researchers Sebastian Bach 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation (2015) — 4,977 citations
• Layer-Wise Relevance Propagation for Deep Neural Network Architectures (2016) — 173 citations
• Analyzing Classifiers: Fisher Vectors and Deep Neural Networks (2015) — 205 citations
• Controlling explanatory heatmap resolution and semantics via decomposition depth (2016) — 25 citations
• Deep Taylor Decomposition of Neural Networks (2016)
• Interpretable Deep Neural Networks for Single-Trial EEG Classification (2016) — 388 citations