James S. Duncan

Artificial Intelligence · 2000–2021

A researcher in the Science corpus: 9 papers; trusts 246 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #12,253 of 35,345 by trust here, #10,065 by citations
• Computer Vision and Pattern Recognition — 139 citations into it; #8,126 of 21,575 by trust here, #9,563 by citations
• Radiology, Nuclear Medicine and Imaging — 83 citations into it
• Optimization & Training — 46 citations into it
• Cognitive Neuroscience — 31 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Biomedical Engineering — 17 citations into it
• Generative Models — 14 citations into it

Whom James S. Duncan trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ilya Sutskever
7. Yann LeCun
8. Karen Simonyan
9. Alex Graves
10. Andrew Zisserman

Who trusts James S. Duncan overall

1. Cemil Kirbas
2. Charles V. Stewart
3. B. van Ginneken
4. Erik H. W. Meijering
5. Francis K. H. Quek
6. Chia-Ling Tsai
7. B. T. H. Romeny
8. Oleh Dzyubachyk
9. B. Roysam
10. Renaud Lopes

Papers from James S. Duncan's perspective

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

Their papers

• AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients (2020) — 669 citations
• Medical Image Analysis: Progress over Two Decades and the Challenges Ahead (2000) — 1,309 citations
• SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation (2021) — 301 citations
• Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results (2020) — 471 citations
• BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis (2020) — 802 citations
• A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020) — 1,021 citations
• Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI (2019) — 309 citations
• Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI (2018) — 79 citations
• Targeted Imaging of the Spatial and Temporal Variation of Matrix Metalloproteinase Activity in Porcine Model of Post-Infarct Remodeling: Relationship to Myocardial Dysfunction (2011) — 108 citations