George C. Linderman

Representation & Self-Supervised Learning · 2017–2021

A researcher in the Science corpus: 10 papers; trusts 38 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 37 citations into it; #936 of 3,145 by trust here, #1,582 by citations
• Molecular Biology — 30 citations into it; #3,691 of 17,773 by trust here, #9,136 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Artificial Intelligence — 22 citations into it
• Biophysics — 14 citations into it
• Computational Mechanics — 14 citations into it
• Computational Theory and Mathematics — 12 citations into it; #3,735 of 12,182 by trust here, #5,239 by citations
• Computational Biology & Bioinformatics — 7 citations into it; #641 of 660 by trust here, #558 by citations

Whom George C. Linderman trusts overall

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

1. L. V. D. van der Maaten
2. Geoffrey E. Hinton
3. Nathan Halko
4. Mikhail Belkin
5. Dmitry Kobak
6. Ulrike von Luxburg
7. Per-Gunnar Martinsson
8. Ronald R. Coifman
9. Partha Niyogi
10. Leland McInnes

Who trusts George C. Linderman overall

1. J. Böhm
2. Dmitry Kobak
3. Philipp Berens
4. Leland McInnes
5. Anna C. Belkina
6. Yingfan Wang
7. Sanjeev Arora
8. Cynthia Rudin
9. M. Rachh
10. Chris Ciccolella

Papers from George C. Linderman's perspective

Ordered by how strongly the researchers George C. Linderman 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Clustering with t-SNE, provably (2017) — 280 citations
• Fast Interpolation-based t-SNE for Improved Visualization of Single-Cell RNA-Seq Data (2017) — 507 citations
• Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations (2019) — 46 citations
• UMAP does not preserve global structure any better than t-SNE when using the same initialization (2019) — 46 citations
• Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science (2017) — 17 citations
• Initialization is critical for preserving global data structure in both t-SNE and UMAP (2021) — 318 citations
• Correction to: Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations (2020)
• Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding (2017) — 102 citations
• Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project) (2017) — 963 citations
• Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis (2017) — 69 citations