Daniel W. Apley

Statistics and Probability · 2002–2018

A researcher in the Science corpus: 7 papers; trusts 36 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 46 citations into it; #2,606 of 12,932 by trust here, #2,965 by citations
• Computational Theory and Mathematics — 44 citations into it; #4,388 of 12,182 by trust here, #4,850 by citations
• Mechanics of Materials — 37 citations into it
• Artificial Intelligence — 28 citations into it; #7,679 of 35,345 by trust here, #16,771 by citations
• Computer Vision and Pattern Recognition — 19 citations into it
• Management Science and Operations Research — 19 citations into it; #5,469 of 11,946 by trust here, #6,888 by citations
• Environmental Engineering — 16 citations into it
• Statistical and Nonlinear Physics — 8 citations into it

Whom Daniel W. Apley trusts overall

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

1. William N. Venables
2. Jerome H. Friedman
3. Alan Y. Chiang
4. Giles Hooker
5. B. D. Ripley
6. Robert B. Gramacy
7. D. Higdon
8. Trevor J. Hastie
9. D. Nychka
10. Edward Snelson

Who trusts Daniel W. Apley overall

1. Q. Liao
2. Mateusz Staniak
3. Maximilian Pichler
4. Christoph Molnar
5. Pantelis Linardatos
6. Linwei Hu
7. Roberto Confalonieri
8. Giorgio Visani
9. Francesco Bodria
10. Haitao Liu

Papers from Daniel W. Apley's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. This PDF file includes: Materials and Methods
5. Generalized Linear Models
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. The Nature of Statistical Learning Theory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A non‐stationary covariance‐based Kriging method for metamodelling in engineering design (2007) — 180 citations
• Local Gaussian Process Approximation for Large Computer Experiments (2013) — 433 citations
• A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality (2017) — 525 citations
• Visualizing the effects of predictor variables in black box supervised learning models (2016) — 1,454 citations
• Quantification of model uncertainty: Calibration, model discrepancy, and identifiability (2012) — 339 citations
• Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques (2018) — 413 citations
• The Autoregressive T2 Chart for Monitoring Univariate Autocorrelated Processes (2002) — 137 citations