Florian Hase
Materials Chemistry · 2015–2022
A researcher in the Science corpus: 7 papers; trusts 37 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 116 citations into it
- Computational Theory and Mathematics — 75 citations into it
- Artificial Intelligence — 64 citations into it
- Atomic and Molecular Physics, and Optics — 50 citations into it
- Molecular Biology — 50 citations into it
- Computational Chemistry & Drug Discovery — 41 citations into it
- Physical and Theoretical Chemistry — 24 citations into it
- Electrical and Electronic Engineering — 21 citations into it
Whom Florian Hase trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Behler
- Matthias Rupp
- Diederik P. Kingma
- A. P. Bart'ok
- Geoffrey E. Hinton
- Jasper Snoek
- Frank Hutter
- Donald R. Jones
- Yoshua Bengio
- Julian Adolphs
Who trusts Florian Hase overall
Papers from Florian Hase's perspective
Ordered by how strongly the researchers Florian Hase 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
- Auto-Encoding Variational Bayes (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- On scientific understanding with artificial intelligence (2022) — 320 citations
- Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation (2019) — 957 citations
- Self-driving laboratory for accelerated discovery of thin-film materials (2019) — 633 citations
- Phoenics: A Bayesian Optimizer for Chemistry (2018) — 306 citations
- Machine-learned potentials for next-generation matter simulations (2021) — 488 citations
- Next-Generation Experimentation with Self-Driving Laboratories (2019) — 307 citations
- Machine learning exciton dynamics (2015) — 86 citations