M. Rachh
Artificial Intelligence · 2017
A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 11 citations into it
- Computational Mechanics — 9 citations into it
- Representation & Self-Supervised Learning — 8 citations into it; #1,971 of 3,145 by trust here, #2,012 by citations
- Computational Theory and Mathematics — 7 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
Whom M. Rachh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nathan Halko
- George C. Linderman
- L. V. D. van der Maaten
- Per-Gunnar Martinsson
- Gal Mishne
- Dmitry Kobak
- Geoffrey E. Hinton
Who trusts M. Rachh overall
- Dmitry Kobak
- Anna C. Belkina
- Yingfan Wang
- Leland McInnes
- Philipp Berens
- Cynthia Rudin
- J. Böhm
- David Chan
Papers from M. Rachh's perspective
Ordered by how strongly the researchers M. Rachh trusts endorse each paper. Same corpus, a different order for every researcher.
- Visualizing Data using t-SNE
- Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Data structures and algorithms for nearest neighbor search in general metric spaces
- Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
- Gradient-based learning applied to document recognition
- A hierarchical O(N log N) force-calculation algorithm
- Going deeper with convolutions
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Their papers
- Fast Interpolation-based t-SNE for Improved Visualization of Single-Cell RNA-Seq Data (2017) — 507 citations
- Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding (2017) — 102 citations