Nicholas Larus-Stone

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 21 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #13,012 of 35,345 by trust here, #13,760 by citations
• Statistical Learning Theory — 22 citations into it
• Information Systems — 19 citations into it; #2,428 of 8,771 by trust here, #2,307 by citations
• Computational Theory and Mathematics — 14 citations into it; #3,885 of 12,182 by trust here, #2,565 by citations
• Algorithms & Complexity — 12 citations into it
• Safety, Robustness & Interpretability — 11 citations into it; #918 of 2,251 by trust here, #1,205 by citations
• Statistics and Probability — 10 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Nicholas Larus-Stone trusts overall

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

1. Cynthia Rudin
2. R. Rivest
3. J. Ross Quinlan
4. Hugh A. Chipman
5. Niko Speybroeck
6. Hongyu Yang
7. Steven L. Salzberg
8. William W. Cohen
9. Benjamin Letham
10. Berk Ustun

Who trusts Nicholas Larus-Stone overall

1. Cynthia Rudin
2. Jimmy J. Lin
3. Alexey Ignatiev
4. Elaine Angelino
5. Margo I. Seltzer
6. Chudi Zhong
7. Tong Wang
8. S. Lo Piano
9. Nina Narodytska
10. Xiyang Hu

Papers from Nicholas Larus-Stone's perspective

Ordered by how strongly the researchers Nicholas Larus-Stone 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Learning Certifiably Optimal Rule Lists (2017) — 201 citations
• Systems Optimizations for Learning Certifiably Optimal Rule Lists (2018)
• Learning Certifiably Optimal Rule Lists: A Case for Discrete Optimization in the 21st Century (2017) — 4 citations