F. Sahli Costabal
Cardiology and Cardiovascular Medicine · 2015–2020
A researcher in the Science corpus: 6 papers; trusts 21 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Cardiology and Cardiovascular Medicine — 29 citations into it; #194 of 906 by trust here, #507 by citations
- Statistics and Probability — 20 citations into it
- Artificial Intelligence — 19 citations into it
- Computational Theory and Mathematics — 17 citations into it
- Statistical and Nonlinear Physics — 10 citations into it
- Probabilistic & Bayesian Methods — 9 citations into it
- Biomedical Engineering — 5 citations into it; #8,643 of 10,734 by trust here, #9,794 by citations
Whom F. Sahli Costabal trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Paris Perdikaris
- Michael L. Stein
- Carl E. Rasmussen
- Dong C. Liu
- Ellen Kuhl
- James Hensman
- G. Em Karniadakis
- Loic Le Gratiet
- Greg Ver Steeg
- Simone Pezzuto
Who trusts F. Sahli Costabal overall
- Paris Perdikaris
- Mark S. Alber
- Ellen Kuhl
- Han Gao
- Grace C. Y. Peng
- Yibo Yang
- Lu Lu
- A. Buganza Tepole
- Benjamin Moseley
- Daniel E. Hurtado
Papers from F. Sahli Costabal's perspective
Ordered by how strongly the researchers F. Sahli Costabal trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Generating Purkinje networks in the human heart (2015) — 118 citations
- Physics-Informed Neural Networks for Cardiac Activation Mapping (2020)
- Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (2019) — 56 citations
- Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification (2019) — 92 citations
- Interpreting Activation Mapping of Atrial Fibrillation: A Hybrid Computational/Physiological Study (2018) — 22 citations
- The importance of mechano-electrical feedback and inertia in cardiac electromechanics (2017) — 66 citations