M. L. Ang

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 1 paper; trusts 36 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 22 citations into it; #16,450 of 21,575 by trust here, #19,766 by citations
• Generative Models — 20 citations into it; #1,556 of 1,956 by trust here, #1,875 by citations
• Artificial Intelligence — 17 citations into it; #26,867 of 35,345 by trust here, #31,361 by citations
• Probabilistic & Bayesian Methods — 10 citations into it
• Statistical and Nonlinear Physics — 10 citations into it; #7,383 of 10,127 by trust here, #8,564 by citations

Whom M. L. Ang trusts overall

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

1. Diederik P. Kingma
2. Felix Otto
3. Geoffrey E. Hinton
4. Qiang Liu
5. Alex Krizhevsky
6. Akinori Tanaka
7. Yoshua Bengio
8. Ian J. Goodfellow
9. Yann LeCun
10. Tim Salimans

Who trusts M. L. Ang overall

1. Weili Nie
2. Arash Vahdat

Papers from M. L. Ang's perspective

Ordered by how strongly the researchers M. L. Ang 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Refining Deep Generative Models via Discriminator Gradient Flow (2020) — 53 citations