Leland McInnes

Representation & Self-Supervised Learning · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 97 researchers and is trusted by 178 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 57 citations into it; #192 of 3,145 by trust here, #244 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #2,857 of 21,575 by trust here, #2,688 by citations
• Artificial Intelligence — 41 citations into it; #9,952 of 35,345 by trust here, #15,542 by citations
• Molecular Biology — 24 citations into it; #16,913 of 17,773 by trust here, #7,496 by citations
• Signal Processing — 20 citations into it; #4,065 of 7,572 by trust here, #6,083 by citations
• Computational Theory and Mathematics — 14 citations into it
• Computational Biology & Bioinformatics — 13 citations into it
• Biophysics — 12 citations into it; #1,150 of 1,196 by trust here, #353 by citations

Whom Leland McInnes trusts overall

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

1. L. V. D. van der Maaten
2. Gunnar E. Carlsson
3. Geoffrey E. Hinton
4. Mikhail Belkin
5. Ricardo José Gabrielli Barreto Campello
6. Joseph B. Kruskal
7. Larry A. Wasserman
8. Daniel Müllner
9. Moo K. Chung
10. George C. Linderman

Who trusts Leland McInnes overall

1. Mohammad Rostami
2. Maarten Grootendorst
3. D. Angelov
4. Pavlin Gregor Policar
5. Dmitry Kobak
6. Edwin S. Dalmaijer
7. Carlos Escolano
8. Christoph Hafemeister
9. Marta Ruiz Costa-jussà
10. Soheil Kolouri

Papers from Leland McInnes's perspective

Ordered by how strongly the researchers Leland McInnes 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. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018) — 12,836 citations
• Dimensionality reduction for visualizing single-cell data using UMAP (2018) — 4,708 citations
• Parametric UMAP Embeddings for Representation and Semisupervised Learning (2020) — 343 citations
• Manifold learning of four-dimensional scanning transmission electron microscopy (2018) — 56 citations
• UMAP: Uniform Manifold Approximation and Projection (2018) — 7,959 citations
• hdbscan: Hierarchical density based clustering (2017) — 2,775 citations
• Accelerated Hierarchical Density Based Clustering (2017) — 519 citations