John Aldo Lee

Representation & Self-Supervised Learning · 2007–2018

A researcher in the Science corpus: 7 papers; trusts 45 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 47 citations into it; #2,712 of 3,145 by trust here, #2,910 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #9,312 of 21,575 by trust here, #12,172 by citations
• Artificial Intelligence — 21 citations into it; #14,399 of 35,345 by trust here, #17,110 by citations
• Computational Theory and Mathematics — 11 citations into it
• Statistical Learning Theory — 8 citations into it
• Cognitive Neuroscience — 7 citations into it
• Signal Processing — 6 citations into it
• Information Retrieval & Search — 5 citations into it

Whom John Aldo Lee trusts overall

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

1. Geoffrey E. Hinton
2. Sam T. Roweis
3. Mark A. Kramer
4. Mikhail Belkin
5. Josh Tenenbaum
6. L. V. D. van der Maaten
7. Lawrence K. Saul
8. Joseph B. Kruskal
9. Kilian Q. Weinberger
10. Partha Niyogi

Who trusts John Aldo Lee overall

1. Michel Verleysen
2. Cyril de Bodt
3. Leland McInnes
4. Matthew O. Williams
5. Mengnan Du
6. Leo Liberti
7. Felix Widmaier
8. Mateus Espadoto
9. Dmitry Kobak
10. Dounia Mulders

Papers from John Aldo Lee's perspective

Ordered by how strongly the researchers John Aldo Lee 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. 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Quality assessment of dimensionality reduction: Rank-based criteria (2009) — 334 citations
• Perplexity-free t-SNE and twice Student tt-SNE (2018) — 20 citations
• Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure (2015) — 98 citations
• Type 1 and 2 mixtures of Kullback-Leibler divergences as cost functions in dimensionality reduction based on similarity preservation (2013) — 102 citations
• Shift-invariant similarities circumvent distance concentration in stochastic neighbor embedding and variants (2011) — 40 citations
• Forecasting the CATS benchmark with the Double Vector Quantization method (2007) — 12 citations
• Nonlinear Dimensionality Reduction (2007) — 409 citations