Josua Krause

Artificial Intelligence · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 26 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #27,174 of 35,345 by trust here, #26,682 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #5,000 of 21,575 by trust here, #6,542 by citations
• Safety, Robustness & Interpretability — 21 citations into it; #801 of 2,251 by trust here, #1,249 by citations
• Information Systems — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Josua Krause trusts overall

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

1. Jerome H. Friedman
2. David Ruppert
3. David Martens
4. Paulo Cortez
5. Harry Zhang
6. Trevor J. Hastie
7. Max Kuhn
8. R. Caruana
9. Todd Kulesza
10. Saleema Amershi

Who trusts Josua Krause overall

1. Minsuk Kahng
2. Sina Mohseni
3. Hendrik Strobelt
4. Sebastian Gehrmann
5. Nadia Burkart
6. Eric D. Ragan
7. Paolo Tamagnini
8. Duen Horng Chau
9. Niloofar Zarei
10. Fred Hohman

Papers from Josua Krause's perspective

Ordered by how strongly the researchers Josua Krause 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations (2017) — 100 citations
• Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations (2017) — 74 citations
• Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models (2016) — 418 citations
• INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data (2014) — 183 citations