Andreas Mayr

Molecular Biology · with Hochreiter · 2010–2018

A researcher in the Science corpus: 8 papers; trusts 162 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 74 citations into it; #2,424 of 17,773 by trust here, #4,543 by citations
• Artificial Intelligence — 73 citations into it; #7,618 of 35,345 by trust here, #5,390 by citations
• Computational Theory and Mathematics — 63 citations into it; #2,575 of 12,182 by trust here, #791 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Model Architectures — 34 citations into it; #622 of 2,278 by trust here, #529 by citations
• Computational Chemistry & Drug Discovery — 31 citations into it; #108 of 425 by trust here, #217 by citations
• Materials Chemistry — 27 citations into it; #533 of 11,513 by trust here, #2,176 by citations
• Representation & Self-Supervised Learning — 23 citations into it

Whom Andreas Mayr trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Ilya Sutskever
5. Yann LeCun
6. J. Urgen Schmidhuber
7. Vinod Nair
8. Ruslan Salakhutdinov
9. Alex Graves
10. Sepp Hochreiter

Who trusts Andreas Mayr overall

1. Garrett B. Goh
2. Thomas Unterthiner
3. Kristina Preuer
4. Esben Jannik Bjerrum
5. Sepp Hochreiter
6. Charles Siegel
7. Dougal Maclaurin
8. Kyle Swanson
9. Günter Klambauer
10. Jingxue Wang

Papers from Andreas Mayr's perspective

Ordered by how strongly the researchers Andreas Mayr 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. 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Self-Normalizing Neural Networks (2017) — 2,892 citations
• Rectified Factor Networks (2015) — 17 citations
• DeepTox: Toxicity Prediction using Deep Learning (2016) — 921 citations
• Toxicity Prediction using Deep Learning (2015) — 165 citations
• Large-scale comparison of machine learning methods for drug target prediction on ChEMBL (2018) — 442 citations
• FABIA: factor analysis for bicluster acquisition (2010) — 336 citations
• Deep Learning as an Opportunity in Virtual Screening (2015) — 183 citations
• cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate (2012) — 445 citations