J. Prutkin

Artificial Intelligence · 2003–2020

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

Subfields

• Artificial Intelligence — 47 citations into it; #28,438 of 35,345 by trust here, #12,083 by citations
• Game & Decision Theory — 5 citations into it

Whom J. Prutkin trusts overall

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

1. Lloyd S. Shapley
2. Scott M. Lundberg

Who trusts J. Prutkin overall

1. Su-In Lee
2. Scott M. Lundberg
3. Juyun Lim

Papers from J. Prutkin's perspective

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

1. Visualizing and Understanding Convolutional Networks
2. Et al
3. Striving for Simplicity: The All Convolutional Net
4. Random Forests
5. Axiomatic Attribution for Deep Networks
6. Scikit-learn: Machine Learning in Python
7. Greedy function approximation: A gradient boosting machine.
8. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
9. Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
10. A Unified Approach to Interpreting Model Predictions

Their papers

• Explainable AI for Trees: From Local Explanations to Global Understanding (2019) — 385 citations
• From Local Explanations to Global Understanding with Explainable AI for Trees (2020) — 8,322 citations
• Labeled scales (e.g., category, Likert, VAS) and invalid across-group comparisons: what we have learned from genetic variation in taste (2003) — 271 citations