Julia Westermayr
Materials Chemistry · 2018–2020
A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 108 citations into it
- Computational Theory and Mathematics — 55 citations into it
- Physical and Theoretical Chemistry — 39 citations into it
- Molecular Biology — 37 citations into it
- Computational Chemistry & Drug Discovery — 31 citations into it
- AI for Physical Sciences — 15 citations into it
- Optimization & Training — 5 citations into it
Whom Julia Westermayr trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Behler
- Xavier Glorot
- Matthias Rupp
- Yoshua Bengio
- Kristof T. Schütt
- A. P. Bart'ok
- Frank Neese
- Geoffrey E. Hinton
- Justin S. Smith
- Radford M. Neal
Who trusts Julia Westermayr overall
Papers from Julia Westermayr's perspective
Ordered by how strongly the researchers Julia Westermayr 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
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Machine learning enables long time scale molecular photodynamics simulations (2018) — 99 citations
- Machine Learning for Electronically Excited States of Molecules (2020) — 340 citations