William Eberle

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #28,255 of 35,345 by trust here, #29,412 by citations
• Safety, Robustness & Interpretability — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Accounting — 6 citations into it
• Economics and Econometrics — 5 citations into it
• Finance — 5 citations into it

Whom William Eberle trusts overall

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

1. Erik Strumbelj
2. Rakesh Agrawal
3. Stan Lipovetsky
4. Scott M. Lundberg
5. Bernhard E. Boser
6. Marco Tulio Ribeiro
7. Trevor J. Hastie
8. Avanti Shrikumar
9. Anupam Datta
10. Jerome H. Friedman

Who trusts William Eberle overall

1. Ningshan Chen
2. Riccardo Guidotti

Papers from William Eberle's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Random Forests
3. Scikit-learn: Machine Learning in Python
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction (2019) — 25 citations
• Genetic algorithms in feature and instance selection (2013) — 237 citations