Katja Hansen

Materials Chemistry · 2009–2015

A researcher in the Science corpus: 8 papers; trusts 10 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 52 citations into it; #1,385 of 11,513 by trust here, #1,810 by citations
• Computational Chemistry & Drug Discovery — 45 citations into it; #80 of 425 by trust here, #35 by citations
• Artificial Intelligence — 36 citations into it; #6,507 of 35,345 by trust here, #3,598 by citations
• Statistical Learning Theory — 19 citations into it
• Statistics and Probability — 15 citations into it
• Control and Systems Engineering — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Katja Hansen trusts overall

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

1. Eric R. Ziegel
2. Yann LeCun
3. L. Eon Bottou
4. Vladimir Naumovich Vapnik
5. Vladimir Naumovich Vapni
6. J. Behler
7. Geoffrey E. Hinton
8. Bernhard Schölkopf
9. Trevor J. Hastie
10. Alex Smola

Who trusts Katja Hansen overall

1. Grgoire Montavon
2. Tânia F Cova
3. Wojciech Samek
4. K. Müller
5. Oliver T. Unke
6. Matthew J. Hirn
7. John C. Snyder
8. Kristof T. Schütt
9. A. Pais
10. Matthias Rupp 

Papers from Katja Hansen's perspective

Ordered by how strongly the researchers Katja Hansen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• How to Explain Individual Classification Decisions (2009) — 1,191 citations
• Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. (2013) — 563 citations
• Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space (2015) — 732 citations
• Finding Density Functionals with Machine Learning (2011) — 507 citations
• Machine learning of molecular electronic properties in chemical compound space (2013) — 659 citations
• Visual Interpretation of Kernel‐Based Prediction Models (2011) — 58 citations
• Benchmark Data Set for in Silico Prediction of Ames Mutagenicity (2009) — 352 citations
• Learning Invariant Representations of Molecules for Atomization Energy Prediction (2012) — 136 citations