Mirco Michel
Molecular Biology · 2014–2018
A researcher in the Science corpus: 5 papers; trusts 62 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 122 citations into it; #3,625 of 17,773 by trust here, #7,384 by citations
- Computational Biology & Bioinformatics — 91 citations into it; #380 of 660 by trust here, #484 by citations
- Materials Chemistry — 31 citations into it; #2,652 of 11,513 by trust here, #6,817 by citations
- Spectroscopy — 16 citations into it
- Genetics — 13 citations into it
- Artificial Intelligence — 10 citations into it
- Computational Theory and Mathematics — 8 citations into it
- Computer Vision and Pattern Recognition — 6 citations into it
Whom Mirco Michel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David C. Jones
- Yang Zhang
- Marcin J. Skwark
- Sergey Ovchinnikov
- Martin Weigt
- David Ménendez Hurtado
- Arne Elofsson
- Sean R. Eddy
- Thomas A. Hopf
- Debora Marks
Who trusts Mirco Michel overall
- Badri Adhikari
- David Ménendez Hurtado
- Arne Elofsson
- Jianlin Cheng
- Joe G. Greener
- Shaun M. Kandathil
- David T. Jones
- Susann Vorberg
- Sheng Wang
- H. Szurmant
Papers from Mirco Michel's perspective
Ordered by how strongly the researchers Mirco Michel 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Dropout: a simple way to prevent neural networks from overfitting
- Gradient-based learning applied to document recognition
- Going deeper with convolutions
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014) — 163 citations
- Predicting accurate contacts in thousands of Pfam domain families using PconsC3 (2017) — 42 citations
- Large-scale structure prediction by improved contact predictions and model quality assessment (2017) — 34 citations
- PconsC4: fast, free, easy, and accurate contact predictions (2018) — 4 citations
- Accurate contact predictions for thousands of protein families using PconsC3 (2016) — 4 citations