Phaedon-Stelios Koutsourelakis

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 95 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #18,893 of 35,345 by trust here, #12,580 by citations
• Statistical and Nonlinear Physics — 53 citations into it; #3,985 of 10,127 by trust here, #1,906 by citations
• Statistics and Probability — 49 citations into it; #5,944 of 12,932 by trust here, #2,407 by citations
• Probabilistic & Bayesian Methods — 44 citations into it
• Computer Vision and Pattern Recognition — 43 citations into it
• Computational Theory and Mathematics — 36 citations into it; #9,895 of 12,182 by trust here, #11,462 by citations
• Computational Mechanics — 34 citations into it
• Generative Models — 24 citations into it

Whom Phaedon-Stelios Koutsourelakis trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Radford M. Neal
2. David John Cameron MacKay
3. Diederik P. Kingma
4. Michael E. Tipping
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Yoshua Bengio
8. Charles M. Bishop
9. Jan S. Hesthaven
10. Zoubin Ghahramani

Who trusts Phaedon-Stelios Koutsourelakis overall

1. Nicholas Geneva
2. Han Gao
3. Luning Sun
4. Nicholas Zabaras
5. Yibo Yang
6. Paris Perdikaris

Papers from Phaedon-Stelios Koutsourelakis's perspective

Ordered by how strongly the researchers Phaedon-Stelios Koutsourelakis trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data (2019) — 1,046 citations
• A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime (2019) — 31 citations
• Predictive Collective Variable Discovery with Deep Bayesian Models (2018) — 36 citations
• Fast uncertainty quantification of activation sequences in patient‐specific cardiac electrophysiology meeting clinical time constraints (2018) — 44 citations
• Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media (2017) — 32 citations