Eric Luhman

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 3 papers; trusts 463 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it; #5,195 of 21,575 by trust here, #9,563 by citations
• Generative Models — 58 citations into it; #477 of 1,956 by trust here, #1,124 by citations
• Artificial Intelligence — 48 citations into it; #7,059 of 35,345 by trust here, #14,438 by citations
• Probabilistic & Bayesian Methods — 20 citations into it
• Statistical and Nonlinear Physics — 16 citations into it; #1,221 of 10,127 by trust here, #2,928 by citations
• Efficiency & Compression — 10 citations into it; #260 of 1,264 by trust here, #758 by citations
• Biophysics — 9 citations into it
• Signal Processing — 9 citations into it

Whom Eric Luhman trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Pascal Vincent
4. Yang Song
5. Jonathan Ho
6. Alex Krizhevsky
7. Yoshua Bengio
8. Alex Graves
9. Jimmy Ba
10. Yann LeCun

Who trusts Eric Luhman overall

1. Tianwei Yin
2. Xingchao Liu
3. Fan Bao
4. Zhisheng Xiao
5. Valentin De Bortoli
6. Kevin Frans
7. Arash Vahdat
8. Zhendong Wang
9. Yuzhang Shang
10. Karsten Kreis

Papers from Eric Luhman's perspective

Ordered by how strongly the researchers Eric Luhman 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed (2021) — 392 citations
• Improving Diffusion Model Efficiency Through Patching (2022) — 23 citations
• Diffusion models for Handwriting Generation (2020) — 34 citations