Bianca Zadrozny

Artificial Intelligence · 2001–2014

A researcher in the Science corpus: 9 papers; trusts 56 researchers and is trusted by 394 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it; #866 of 35,345 by trust here, #2,700 by citations
• Statistical Learning Theory — 14 citations into it; #94 of 1,038 by trust here, #243 by citations
• Reinforcement Learning — 13 citations into it; #1,717 of 2,242 by trust here, #1,973 by citations
• Natural Language Processing — 10 citations into it; #984 of 5,688 by trust here, #2,385 by citations
• Optimization & Training — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it; #148 of 3,147 by trust here, #1,132 by citations
• Algorithms & Complexity — 6 citations into it

Whom Bianca Zadrozny trusts overall

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

1. Charles Peter Elkan
2. J. Ross Quinlan
3. Thorsten Joachims
4. Leo Breiman
5. Christopher K. I. Williams
6. James J. Heckman
7. Charles M. Bishop
8. Trevor J. Hastie
9. Subir Ghosh
10. Yoav Freund

Who trusts Bianca Zadrozny overall

1. Charu C. Aggarwal
2. Tom Fawcett
3. Mahdi Pakdaman Naeini
4. Eyke Hüllermeier
5. Amos J. Storkey
6. Andrea Dal Pozzolo
7. Charles Peter Elkan
8. R. Caruana
9. Nitesh V. Chawla
10. Alexandru Niculescu-Mizil

Papers from Bianca Zadrozny's perspective

Ordered by how strongly the researchers Bianca Zadrozny 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Transforming classifier scores into accurate multiclass probability estimates (2002) — 1,705 citations
• Sequential cost-sensitive decision making with reinforcement learning (2002) — 89 citations
• Learning and evaluating classifiers under sample selection bias (2004) — 921 citations
• Cost-sensitive learning by cost-proportionate example weighting (2003) — 758 citations
• Relating reinforcement learning performance to classification performance (2005) — 66 citations
• Learning Character-level Representations for Part-of-Speech Tagging (2014) — 606 citations
• Outlier detection by active learning (2006) — 368 citations
• Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers (2001) — 1,118 citations
• Learning and making decisions when costs and probabilities are both unknown (2001) — 537 citations