Nathaniel Fruchter

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it
• Safety, Robustness & Interpretability — 14 citations into it

Whom Nathaniel Fruchter trusts overall

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

1. Jerome H. Friedman
2. Cynthia Rudin
3. Erik Strumbelj
4. Been Kim
5. Qingyuan Zhao
6. Bradley C. Boehmke

Papers from Nathaniel Fruchter's perspective

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

1. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. How transferable are features in deep neural networks?
5. Learning Deep Features for Discriminative Localization
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Greedy function approximation: A gradient boosting machine.
8. Attention is All you Need (Neural Information Processing Systems)
9. Gradient-based learning applied to document recognition
10. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Their papers

• Explaining Explanations to Society (2019) — 36 citations
• "My Data Just Goes Everywhere: " User Mental Models of the Internet and Implications for Privacy and Security (2015) — 354 citations