M. J. Lee

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 8 papers; trusts 179 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 98 citations into it; #14,721 of 21,575 by trust here, #4,152 by citations
• Artificial Intelligence — 77 citations into it; #24,089 of 35,345 by trust here, #5,002 by citations
• Cognitive Neuroscience — 40 citations into it
• Graph Learning — 35 citations into it
• Model Architectures — 23 citations into it
• Generative Models — 15 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Computational Mechanics — 11 citations into it

Whom M. J. Lee trusts overall

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

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

Who trusts M. J. Lee overall

1. Arvid Lundervold
2. Fei Ye
3. Wentao Zhu

Papers from M. J. Lee's perspective

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

Their papers

• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders (2016) — 734 citations
• DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks (2016) — 410 citations
• Attention U-Net: Learning Where to Look for the Pancreas (2018) — 7,627 citations
• Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks (2017) — 216 citations
• Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease (2018) — 743 citations
• Metric learning with spectral graph convolutions on brain connectivity networks (2018) — 375 citations
• Spectral Graph Convolutions for Population-based Disease Prediction (2017) — 250 citations
• DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images (2017) — 85 citations