A. P. Bart'ok
Materials Chemistry · 2009–2021
A researcher in the Science corpus: 9 papers; trusts 26 researchers and is trusted by 91 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 183 citations into it; #93 of 11,513 by trust here, #1,049 by citations
- Computational Theory and Mathematics — 87 citations into it; #1,746 of 12,182 by trust here, #445 by citations
- Atomic and Molecular Physics, and Optics — 86 citations into it; #145 of 7,135 by trust here, #352 by citations
- Computational Chemistry & Drug Discovery — 50 citations into it; #13 of 425 by trust here, #23 by citations
- Molecular Biology — 50 citations into it; #4,385 of 17,773 by trust here, #8,570 by citations
- Physical and Theoretical Chemistry — 47 citations into it
- AI for Physical Sciences — 25 citations into it
- Electrical and Electronic Engineering — 20 citations into it
Whom A. P. Bart'ok trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Behler
- Risi Kondor
- Christopher K. I. Williams
- Gábor Csányi
- Alexander V. Shapeev
- David John Cameron MacKay
- Volker L. Deringer
- S. Plimpton
- Nongnuch Artrith
- Marco Cuturi
Who trusts A. P. Bart'ok overall
- Alexander V. Shapeev
- Matthew J. Hirn
- Xianglin Liu
- Gábor Csányi
- Johannes Klicpera
- ST John
- Pascal Friederich
- Yuri Mishin
- Kristof T. Schütt
- Aldo Glielmo
Papers from A. P. Bart'ok's perspective
Ordered by how strongly the researchers A. P. Bart'ok 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- On representing chemical environments (2012) — 1,908 citations
- Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. (2009) — 2,337 citations
- Machine learning unifies the modeling of materials and molecules (2017) — 652 citations
- Comparing molecules and solids across structural and alchemical space. (2015) — 593 citations
- Accuracy and transferability of Gaussian approximation potential models for tungsten (2014) — 247 citations
- Gaussian approximation potentials: A brief tutorial introduction (2015) — 556 citations
- Machine Learning a General-Purpose Interatomic Potential for Silicon (2018) — 457 citations
- Gaussian Process Regression for Materials and Molecules (2021) — 1,025 citations
- Efficient sampling of atomic configurational spaces. (2009) — 101 citations