Jared A. Dunnmon

Artificial Intelligence · 2011–2020

A researcher in the Science corpus: 9 papers; trusts 267 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it; #12,790 of 35,345 by trust here, #6,911 by citations
• Computer Vision and Pattern Recognition — 91 citations into it
• Representation & Self-Supervised Learning — 36 citations into it; #1,702 of 3,145 by trust here, #1,493 by citations
• Evaluation & Benchmarks — 27 citations into it
• Natural Language Processing — 27 citations into it
• Model Architectures — 14 citations into it
• Biomedical Engineering — 11 citations into it
• Generative Models — 11 citations into it

Whom Jared A. Dunnmon trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Andrea Vedaldi
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Diederik P. Kingma
9. Andrew Zisserman
10. Ross B. Girshick

Who trusts Jared A. Dunnmon overall

1. Alexander J. Ratner
2. Ekin Dogus Cubuk
3. Heang-Ping Chan
4. Nimit Sharad Sohoni
5. Barret Zoph
6. Christopher R'e
7. Elliot Creager
8. Raphael Gontijo Lopes
9. Lubomir M. Hadjiiski

Papers from Jared A. Dunnmon's perspective

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

Their papers

• Learning to Compose Domain-Specific Transformations for Data Augmentation (2017) — 367 citations
• Hidden stratification causes clinically meaningful failures in machine learning for medical imaging (2019) — 531 citations
• No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems (2020) — 306 citations
• Training Complex Models with Multi-Task Weak Supervision (2018) — 227 citations
• Power extraction from aeroelastic limit cycle oscillations (2011) — 242 citations
• Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. (2019) — 203 citations
• Snorkel MeTaL: Weak Supervision for Multi-Task Learning (2018)
• Snorkel MeTaL: Weak Supervision for Multi-Task Learning (2018) — 61 citations
• Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences (2018) — 20 citations