Christof Angermueller

Molecular Biology · 2014–2025

A researcher in the Science corpus: 6 papers; trusts 112 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 73 citations into it; #3,980 of 17,773 by trust here, #3,389 by citations
• Artificial Intelligence — 50 citations into it
• Computer Vision and Pattern Recognition — 48 citations into it
• Optimization & Training — 24 citations into it
• Model Architectures — 23 citations into it
• Natural Language Processing — 11 citations into it
• Biophysics — 10 citations into it; #404 of 1,196 by trust here, #228 by citations
• Computational Biology & Bioinformatics — 9 citations into it; #333 of 660 by trust here, #326 by citations

Whom Christof Angermueller trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Diederik P. Kingma
5. Karen Simonyan
6. Yann LeCun
7. Ilya Sutskever
8. Alex Krizhevsky
9. James Bergstra
10. Ruslan Salakhutdinov

Who trusts Christof Angermueller overall

1. Andrey Kan
2. Eirini Arvaniti
3. William Grant Hatcher
4. Qizhi He
5. Daniel Quang
6. Umut Eser
7. R. Argelaguet
8. Quan Zou
9. Pang Wei Koh
10. Marta M. Stepniewska-Dziubinska

Papers from Christof Angermueller's perspective

Ordered by how strongly the researchers Christof Angermueller 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. 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

• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Deep learning for computational biology (2016) — 1,263 citations
• Single-Cell Genome-Wide Bisulfite Sequencing for Assessing Epigenetic Heterogeneity (2014) — 1,047 citations
• Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity (2015) — 648 citations
• DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning (2016) — 488 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations