Randal S. Olson

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 5 papers; trusts 18 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #4,609 of 35,345 by trust here, #6,911 by citations
• Genetics — 18 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Information Systems — 13 citations into it
• Molecular Biology — 13 citations into it
• Optimization & Training — 12 citations into it
• Statistics and Probability — 12 citations into it
• Computational Theory and Mathematics — 11 citations into it; #4,299 of 12,182 by trust here, #2,104 by citations

Whom Randal S. Olson trusts overall

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

1. Ryan J. Urbanowicz
2. Wolfgang Banzhaf
3. Félix-Antoine Fortin
4. James Franklin
5. David Ruppert
6. Jeff Kiralis
7. Jason H. Moore
8. Fabian Pedregosa
9. Kalyanmoy Deb
10. François-Michel De Rainville

Who trusts Randal S. Olson overall

1. Marc-André Zöller
2. Marco F. Huber
3. Quanming Yao
4. Iddo Drori
5. Matthias Feurer
6. Mohamed Maher
7. Harvey Wu
8. Li Yang
9. Mengshuo Wang
10. Davide Chicco

Papers from Randal S. Olson's perspective

Ordered by how strongly the researchers Randal S. Olson 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. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Attention is All you Need (Neural Information Processing Systems)
10. Long Short-Term Memory

Their papers

• TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning (2016) — 712 citations
• PMLB: a large benchmark suite for machine learning evaluation and comparison (2017) — 459 citations
• Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science (2016) — 620 citations
• Automating Biomedical Data Science Through Tree-Based Pipeline Optimization (2016) — 336 citations
• Relief-Based Feature Selection: Introduction and Review (2017) — 1,180 citations