Mikkel N. Schmidt

Materials Chemistry · 2006–2018

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 15 citations into it; #7,572 of 11,513 by trust here, #10,845 by citations
• Computational Theory and Mathematics — 14 citations into it; #9,592 of 12,182 by trust here, #9,306 by citations
• Artificial Intelligence — 11 citations into it; #26,479 of 35,345 by trust here, #31,361 by citations
• Computational Chemistry & Drug Discovery — 9 citations into it; #292 of 425 by trust here, #393 by citations
• Molecular Biology — 9 citations into it
• Graph Learning — 8 citations into it; #546 of 693 by trust here, #650 by citations
• Computer Vision and Pattern Recognition — 6 citations into it
• Statistical and Nonlinear Physics — 5 citations into it; #7,231 of 10,127 by trust here, #8,564 by citations

Whom Mikkel N. Schmidt trusts overall

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

1. Sam T. Roweis
2. Daniel D. Lee
3. Barbara Olshausen
4. Julian Eggert
5. Martin P. Cooke
6. David J. Field
7. Kristof T. Schütt
8. Mark Newman
9. Edgar Körner
10. Diederik P. Kingma

Who trusts Mikkel N. Schmidt overall

1. Dong Yu
2. Chao Weng
3. Emad M. Grais
4. Jing-jing Chen
5. J. Kolter
6. Morten Kolbaek
7. Hakan Erdogan
8. Chenxing Li
9. Jing Shi
10. Björn Schuller

Papers from Mikkel N. Schmidt's perspective

Ordered by how strongly the researchers Mikkel N. Schmidt 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Going deeper with convolutions
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Infinite multiple membership relational modeling for complex networks (2011) — 64 citations
• Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials (2018) — 86 citations
• Single-channel speech separation using sparse non-negative matrix factorization (2006) — 411 citations