Frank Emmert-Streib

Artificial Intelligence · 2007–2020

A researcher in the Science corpus: 4 papers; trusts 40 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it
• Molecular Biology — 39 citations into it; #5,438 of 17,773 by trust here, #10,864 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Model Architectures — 23 citations into it
• Natural Language Processing — 13 citations into it
• Genetics — 10 citations into it
• Signal Processing — 10 citations into it
• Optimization & Training — 9 citations into it

Whom Frank Emmert-Streib trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Paul J. Werbos
5. Yann LeCun
6. J. Urgen Schmidhuber
7. Sepp Hochreiter
8. L. Eon Bottou
9. Alex Krizhevsky
10. Karen Simonyan

Who trusts Frank Emmert-Streib overall

1. Joris M. Mooij
2. Ricardo M. A. Silva
3. Barbara Rakitsch
4. Nicholas Cornia

Papers from Frank Emmert-Streib's perspective

Ordered by how strongly the researchers Frank Emmert-Streib 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• A review of connectivity map and computational approaches in pharmacogenomics (2017) — 251 citations
• Harnessing naturally randomized transcription to infer regulatory relationships among genes (2007) — 143 citations
• An Introductory Review of Deep Learning for Prediction Models With Big Data (2020) — 492 citations
• Entropy Bounds for Hierarchical Molecular Networks (2008) — 363 citations