Stefan Steinerberger

Representation & Self-Supervised Learning · 2015–2022

A researcher in the Science corpus: 8 papers; trusts 28 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 24 citations into it; #1,054 of 3,145 by trust here, #1,638 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Artificial Intelligence — 20 citations into it
• Molecular Biology — 19 citations into it; #3,647 of 17,773 by trust here, #9,136 by citations
• Computational Mechanics — 12 citations into it
• Computational Theory and Mathematics — 8 citations into it; #3,736 of 12,182 by trust here, #5,239 by citations
• Signal Processing — 8 citations into it
• Computational Biology & Bioinformatics — 5 citations into it; #639 of 660 by trust here, #558 by citations

Whom Stefan Steinerberger trusts overall

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

1. L. V. D. van der Maaten
2. Geoffrey E. Hinton
3. George C. Linderman
4. Nathan Halko
5. Stéphane Mallat
6. Alex Krizhevsky
7. Yoshua Bengio
8. Mikhail Belkin
9. Benjamin M. Schmidt
10. Sam T. Roweis

Who trusts Stefan Steinerberger overall

1. George C. Linderman
2. J. Böhm
3. Dmitry Kobak
4. Philipp Berens
5. Anna C. Belkina
6. Sanjeev Arora
7. Leland McInnes
8. Sebastian Lapuschkin
9. Yingfan Wang

Papers from Stefan Steinerberger's perspective

Ordered by how strongly the researchers Stefan Steinerberger 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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 Large Scale Visual Recognition Challenge
9. ImageNet: A large-scale hierarchical image database
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
• Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science (2017) — 17 citations
• Neural collapse under cross-entropy loss (2022)
• 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
• Nonlinear Phase Unwinding of Functions (2015) — 66 citations