F. Kingma

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 124 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 36 citations into it
• Artificial Intelligence — 34 citations into it
• Generative Models — 29 citations into it
• Probabilistic & Bayesian Methods — 15 citations into it
• Optimization & Training — 11 citations into it
• Signal Processing — 9 citations into it
• Model Architectures — 7 citations into it
• Efficiency & Compression — 5 citations into it; #676 of 1,264 by trust here, #1,116 by citations

Whom F. Kingma trusts overall

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

1. Diederik P. Kingma
2. Tim Salimans
3. Geoffrey E. Hinton
4. Aäron van den Oord
5. Yoshua Bengio
6. Kaiming He
7. Alex Krizhevsky
8. Nal Kalchbrenner
9. Alex Graves
10. Jimmy Ba

Who trusts F. Kingma overall

1. Shifeng Zhang
2. Jonathan Ho
3. Emiel Hoogeboom
4. Simon Washington
5. James Townsend
6. Zhihao Duan
7. Thomas Bird
8. Rianne van den Berg
9. Evan Lohn
10. Chen Zhang

Papers from F. Kingma's perspective

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

Their papers

• Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks (2020) — 10 citations
• Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables (2019) — 113 citations