Anna Goldenberg

Molecular Biology · 2009–2021

A researcher in the Science corpus: 14 papers; trusts 216 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 264 citations into it
• Artificial Intelligence — 172 citations into it; #21,532 of 35,345 by trust here, #7,531 by citations
• Statistical and Nonlinear Physics — 71 citations into it; #3,950 of 10,127 by trust here, #3,010 by citations
• Computational Theory and Mathematics — 59 citations into it
• Genetics — 57 citations into it
• Computer Vision and Pattern Recognition — 55 citations into it; #19,545 of 21,575 by trust here, #13,269 by citations
• Safety, Robustness & Interpretability — 44 citations into it
• Representation & Self-Supervised Learning — 35 citations into it

Whom Anna Goldenberg trusts overall

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

1. Tom A. B. Snijders
2. Mark Newman
3. Diederik P. Kingma
4. Mark S. Handcock
5. Geoffrey E. Hinton
6. Jon M. Kleinberg
7. David R. Hunter
8. Yoshua Bengio
9. Jure Leskovec
10. Alex Krizhevsky

Who trusts Anna Goldenberg overall

1. Jiashun Jin
2. Saket Navlakha
3. Jing Lei
4. Scott W. Linderman
5. Tuan M. V. Le
6. Prem Gopalan
7. Hao Wang
8. Yunpeng Zhao
9. Víctor Martínez
10. Daniel Lewis Sussman

Papers from Anna Goldenberg's perspective

Ordered by how strongly the researchers Anna Goldenberg 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. Gradient-based learning applied to document recognition
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

• Explaining Image Classifiers by Counterfactual Generation (2018) — 298 citations
• Similarity network fusion for aggregating data types on a genomic scale (2014) — 1,802 citations
• Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018) — 586 citations
• Dropout Feature Ranking for Deep Learning Models (2017) — 53 citations
• Machine learning approaches to drug response prediction: challenges and recent progress (2020) — 326 citations
• Sensitive tumour detection and classification using plasma cell-free DNA methylomes (2018) — 821 citations
• Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding (2021) — 402 citations
• Transparency and reproducibility in artificial intelligence (2020) — 429 citations
• What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use (2019) — 663 citations
• Do no harm: a roadmap for responsible machine learning for health care (2019) — 925 citations
• Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks (2019) — 131 citations
• Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL (2018) — 5 citations
• Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma (2017) — 1,688 citations
• A Survey of Statistical Network Models (2009) — 1,002 citations