Bradley A. Malin

Artificial Intelligence · 2004–2018

A researcher in the Science corpus: 12 papers; trusts 54 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #12,672 of 35,345 by trust here, #8,007 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #12,474 of 21,575 by trust here, #8,409 by citations
• Generative Models — 33 citations into it; #1,847 of 1,956 by trust here, #989 by citations
• Management Science and Operations Research — 18 citations into it; #6,541 of 11,946 by trust here, #7,262 by citations
• Computational Mathematics — 17 citations into it; #382 of 612 by trust here, #210 by citations
• Molecular Biology — 17 citations into it
• Health Information Management — 13 citations into it
• Computational Mechanics — 12 citations into it

Whom Bradley A. Malin trusts overall

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

1. Latanya Sweeney
2. Diederik P. Kingma
3. Ian J. Goodfellow
4. Ronald J. Williams
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Kaiming He
8. Alex Krizhevsky
9. Yann LeCun
10. Jimmy Ba

Who trusts Bradley A. Malin overall

1. Arvind Narayanan
2. Edward Choi
3. Vitaly Shmatikov
4. James Jordon
5. Chloé-Agathe Azencott
6. Peter Christen
7. B. Lo
8. Latanya Sweeney
9. Samuel A. Assefa
10. Mehmet Kuzu

Papers from Bradley A. Malin's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Preserving privacy by de-identifying face images (2005) — 602 citations
• A Constraint Satisfaction Cryptanalysis of Bloom Filters in Private Record Linkage (2011) — 121 citations
• A practical approach to achieve private medical record linkage in light of public resources (2013) — 64 citations
• Rubik: Knowledge Guided Tensor Factorization and Completion for Health Data Analytics (2015) — 179 citations
• It’s all in the timing: calibrating temporal penalties for biomedical data sharing (2018) — 12 citations
• Generating Multi-label Discrete Patient Records using Generative Adversarial Networks (2017) — 725 citations
• Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks (2017) — 78 citations
• Correction: A Systematic Review of Re-Identification Attacks on Health Data (2015) — 148 citations
• Limestone: High-throughput candidate phenotype generation via tensor factorization (2014) — 164 citations
• The disclosure of diagnosis codes can breach research participants' privacy (2010) — 143 citations
• Anonymization of electronic medical records for validating genome-wide association studies (2010) — 137 citations
• How (not) to protect genomic data privacy in a distributed network: using trail re-identification to evaluate and design anonymity protection systems (2004) — 280 citations