Ilias Bilionis

Statistics and Probability · 2013–2019

A researcher in the Science corpus: 3 papers; trusts 82 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 48 citations into it; #2,634 of 12,932 by trust here, #2,629 by citations
• Artificial Intelligence — 37 citations into it; #13,753 of 35,345 by trust here, #17,412 by citations
• Computational Theory and Mathematics — 24 citations into it; #2,377 of 12,182 by trust here, #3,033 by citations
• Optimization & Training — 18 citations into it; #1,203 of 2,601 by trust here, #1,766 by citations
• Statistical and Nonlinear Physics — 16 citations into it; #2,042 of 10,127 by trust here, #2,928 by citations
• Computer Vision and Pattern Recognition — 14 citations into it
• Management Science and Operations Research — 14 citations into it
• Environmental Engineering — 12 citations into it

Whom Ilias Bilionis trusts overall

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

1. Dongbin Xiu
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Graves
5. Matthew D. Zeiler
6. Yoshua Bengio
7. Charles F. Van Loan
8. L. Eon Bottou
9. G. Em Karniadakis
10. Yann LeCun

Who trusts Ilias Bilionis overall

1. Constantin Grigo
2. Yinhao Zhu
3. Yibo Yang
4. Sharmila Karumuri
5. Phaedon-Stelios Koutsourelakis
6. Edward Meeds
7. Nicholas Zabaras
8. Rohit Tripathy
9. Paris Perdikaris
10. Jared D. Willard

Papers from Ilias Bilionis's perspective

Ordered by how strongly the researchers Ilias Bilionis trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification (2013) — 181 citations
• Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (2018) — 462 citations
• Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (2019) — 154 citations