V. Botu
Materials Chemistry · 2015–2016
A researcher in the Science corpus: 3 papers; trusts 8 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 26 citations into it; #1,024 of 11,513 by trust here, #4,767 by citations
- Computational Chemistry & Drug Discovery — 11 citations into it
- Statistical Learning Theory — 10 citations into it
- Molecular Biology — 9 citations into it; #4,277 of 17,773 by trust here, #9,367 by citations
- Computational Theory and Mathematics — 7 citations into it; #4,440 of 12,182 by trust here, #4,696 by citations
- Statistics and Probability — 7 citations into it
- Artificial Intelligence — 6 citations into it
- AI for Physical Sciences — 5 citations into it
Whom V. Botu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Behler
- Klaus Nordhausen
- Matthias Rupp
- Roberto Car
- Ellad B. Tadmor
- Trevor J. Hastie
- Katja Hansen
- Michele Parrinello
- A. P. Bart'ok
- Ronald E. Miller
Who trusts V. Botu overall
- Yuri Mishin
- G. P. P. Pun
- Rohit Batra
- Rampi Ramprasad
- Tânia F Cova
- Yu-Hang Tang
- Evgeny V. Podryabinkin
- A. Pais
- An Chen
- Aldo Glielmo
Papers from V. Botu's perspective
Ordered by how strongly the researchers V. Botu trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- ImageNet: A large-scale hierarchical image database
- The Elements of Statistical Learning
- The elements of statistical learning: data mining, inference, and prediction, 2nd Edition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
- The Nature of Statistical Learning Theory
Their papers
- Learning scheme to predict atomic forces and accelerate materials simulations (2015) — 183 citations
- Adaptive machine learning framework to accelerate ab initio molecular dynamics (2015) — 374 citations
- Machine Learning Force Fields: Construction, Validation, and Outlook (2016) — 485 citations