Katherine A. Heller

Artificial Intelligence · 2005–2022

A researcher in the Science corpus: 13 papers; trusts 145 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 270 citations into it; #20,143 of 35,345 by trust here, #6,911 by citations
• Computer Vision and Pattern Recognition — 93 citations into it
• Safety, Robustness & Interpretability — 74 citations into it
• Evaluation & Benchmarks — 61 citations into it
• Representation & Self-Supervised Learning — 54 citations into it
• Probabilistic & Bayesian Methods — 51 citations into it; #1,290 of 1,525 by trust here, #945 by citations
• Medical & Health AI — 40 citations into it; #159 of 239 by trust here, #177 by citations
• Statistics and Probability — 33 citations into it

Whom Katherine A. Heller trusts overall

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

1. Radford M. Neal
2. Diederik P. Kingma
3. Carl E. Rasmussen
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Alex Krizhevsky
7. Kaiming He
8. J. Urgen Schmidhuber
9. Yann LeCun
10. Michael I. Jordan

Who trusts Katherine A. Heller overall

1. T. Griffiths
2. Zoubin Ghahramani
3. Ryan P. Adams
4. Gerry Tonkin-Hill
5. Michael I. Jordan
6. Mehrdad Farajtabar
7. Mikhail Yurochkin
8. John Lees
9. Stefan Harmeling
10. Dong Yin

Papers from Katherine A. Heller's perspective

Ordered by how strongly the researchers Katherine A. Heller 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Analyzing the role of model uncertainty for electronic health records (2019) — 123 citations
• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors (2020) — 240 citations
• Online gradient-based mixtures for transfer modulation in meta-learning (2018) — 12 citations
• An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection (2017) — 142 citations
• Evaluation Gaps in Machine Learning Practice (2022) — 101 citations
• Do no harm: a roadmap for responsible machine learning for health care (2019) — 925 citations
• Modulating transfer between tasks in gradient-based meta-learning (2018) — 7 citations
• Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks (2018) — 22 citations
• Reconciling meta-learning and continual learning with online mixtures of tasks (2018) — 113 citations
• Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier (2017) — 194 citations
• The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling (2010) — 167 citations
• Bayesian hierarchical clustering (2005) — 419 citations