Holger Fröhlich
Artificial Intelligence · 2003–2018
A researcher in the Science corpus: 4 papers; trusts 12 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 27 citations into it; #16,267 of 35,345 by trust here, #21,316 by citations
- Molecular Biology — 14 citations into it; #7,325 of 17,773 by trust here, #13,284 by citations
- Computational Theory and Mathematics — 9 citations into it; #8,124 of 12,182 by trust here, #7,142 by citations
- Statistical Learning Theory — 9 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it; #12,209 of 21,575 by trust here, #16,367 by citations
Whom Holger Fröhlich trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ron Kohavi
- Vladimir Naumovich Vapnik
- George H. John
- Vladimir Naumovich Vapni
- David E. Goldberg
- Olivier Chapelle
- Jason Aaron Edward Weston
- Avrim Blum
- Bernhard Schölkopf
- Francis R. Bach
Who trusts Holger Fröhlich overall
- Nils M. Kriege
- Fredrik D. Johansson
- Frauke Friedrichs
- Ezgi Zorarpacı
- D. Basak
- Xifeng Yan
- Christian Igel
- Selma Ayse Özel
- Mehmet Fatih Akay
- Ulrich Rückert
Papers from Holger Fröhlich's perspective
Ordered by how strongly the researchers Holger Fröhlich trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Optimal assignment kernels for attributed molecular graphs (2005) — 190 citations
- Towards Optimal Descriptor Subset Selection with Support Vector Machines in Classification and Regression (2004) — 39 citations
- From hype to reality: data science enabling personalized medicine (2018) — 359 citations
- Feature selection for support vector machines by means of genetic algorithm (2003) — 350 citations