Bradley James Erickson

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 8 papers; trusts 13 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 57 citations into it; #21,047 of 21,575 by trust here, #9,435 by citations
• Media Technology — 8 citations into it

Whom Bradley James Erickson trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Ian J. Goodfellow
4. Yann LeCun
5. Yoshua Bengio
6. Christian Szegedy
7. Ilya Sutskever
8. Nitish Srivastava
9. Kaiming He
10. Ruslan Salakhutdinov

Who trusts Bradley James Erickson overall

1. Arvid Lundervold
2. Mufti Mahmud
3. J. Beton
4. Mainak Biswas
5. M. S. Kaiser

Papers from Bradley James Erickson's perspective

Ordered by how strongly the researchers Bradley James Erickson 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. ImageNet: A large-scale hierarchical image database
4. ImageNet Large Scale Visual Recognition Challenge
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. (2019) — 330 citations
• The RSNA Pediatric Bone Age Machine Learning Challenge. (2019) — 442 citations
• Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning. (2019) — 314 citations
• Toolkits and Libraries for Deep Learning (2017) — 164 citations
• Machine Learning for Medical Imaging (2017) — 1,023 citations
• Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys (2017) — 146 citations
• Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions (2017) — 1,026 citations
• Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. (2015) — 646 citations