Markus Meuwly
Atomic and Molecular Physics, and Optics · 2012–2021
A researcher in the Science corpus: 5 papers; trusts 24 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Atomic and Molecular Physics, and Optics — 59 citations into it; #6,131 of 7,135 by trust here, #4,892 by citations
- Materials Chemistry — 52 citations into it; #1,763 of 11,513 by trust here, #5,923 by citations
- Computational Chemistry & Drug Discovery — 34 citations into it; #141 of 425 by trust here, #231 by citations
- Computational Theory and Mathematics — 32 citations into it; #3,146 of 12,182 by trust here, #1,633 by citations
- Molecular Biology — 32 citations into it; #4,225 of 17,773 by trust here, #7,980 by citations
- Physical and Theoretical Chemistry — 31 citations into it
- Artificial Intelligence — 25 citations into it
- AI for Physical Sciences — 15 citations into it
Whom Markus Meuwly trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Simon S. Haykin
- J. Behler
- Matthias Rupp
- Alex Smola
- Alain F. Berlinet
- Bernhard Schölkopf
- Yoshua Bengio
- Kaiming He
- Geoffrey E. Hinton
- Alexandre Tkatchenko
Who trusts Markus Meuwly overall
Papers from Markus Meuwly's perspective
Ordered by how strongly the researchers Markus Meuwly 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Et al
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. (2019) — 1,056 citations
- Machine Learning for Chemical Reactions. (2021) — 350 citations
- A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information. (2018) — 88 citations
- Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces (2017) — 121 citations
- Atomic multipoles: Electrostatic potential fit, local reference axis systems, and conformational dependence (2012) — 68 citations