Jef Caers
Environmental Engineering · 2006–2017
A researcher in the Science corpus: 13 papers; trusts 72 researchers and is trusted by 36 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 38 citations into it; #133 of 5,479 by trust here, #1,116 by citations
- Ocean Engineering — 26 citations into it; #284 of 3,193 by trust here, #1,204 by citations
- Artificial Intelligence — 20 citations into it; #5,346 of 35,345 by trust here, #15,866 by citations
- Computer Vision and Pattern Recognition — 20 citations into it
- Mechanical Engineering — 17 citations into it; #1,007 of 8,300 by trust here, #6,077 by citations
- Statistics and Probability — 16 citations into it
- Geophysics — 9 citations into it
- Probabilistic & Bayesian Methods — 6 citations into it
Whom Jef Caers trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sébastien Strebelle
- André G. Journel
- Julian Besag
- Satomi Suzuki
- Grace Wahba
- Christoph H. Lampert
- Mark A. Girolami
- Roger N. Shepard
- S. Krishnan
- Anil K. Jain
Who trusts Jef Caers overall
- Philippe Renard
- Gregoire Mariethoz
- A. Alcolea
- Niklas Linde
- Pejman Tahmasebi
- Hai X. Vo
- Louis J. Durlofsky
- Satomi Suzuki
- Tapan Mukerji
- Tiangang Cui
Papers from Jef Caers's perspective
Ordered by how strongly the researchers Jef Caers 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Discovering geochemical patterns by factor-based cluster analysis (2017) — 52 citations
- Geological realism in hydrogeological and geophysical inverse modeling: A review (2015) — 195 citations
- Direct forecasting of subsurface flow response from non-linear dynamic data by linear least-squares in canonical functional principal component space (2014) — 68 citations
- Multiple-point Geostatistics: Stochastic Modeling with Training Images (2014) — 448 citations
- Modeling Spatial Uncertainty (2011) — 152 citations
- Modeling Uncertainty in the Earth Sciences: Caers/Modeling Uncertainty in the Earth Sciences (2011) — 172 citations
- Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling (2010) — 323 citations
- Bayesian inverse problem and optimization with iterative spatial resampling (2010) — 142 citations
- Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization (2008) — 111 citations
- A Distance-based Prior Model Parameterization for Constraining Solutions of Spatial Inverse Problems (2008) — 88 citations
- Conditional Simulation with Patterns (2007) — 307 citations
- Comparing the Gradual Deformation with the Probability Perturbation Method for Solving Inverse Problems (2007) — 52 citations
- The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling (2006) — 154 citations