References

July 19, 2026 ยท View on GitHub

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References are listed in AACR journal style. Software projects are included when they influenced design, benchmarking, or backend engineering.

  1. van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res 2008;9:2579-2605.
  2. van der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res 2014;15:3221-3245.
  3. Linderman GC, Rachh M, Hoskins JG, Steinerberger S, Kluger Y. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nat Methods 2019;16:243-245.
  4. Policar PG, Strazar M, Zupan B. openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding. J Open Source Softw 2019;4:1576.
  5. Chan DM, Rao R, Huang F, Canny JF. t-SNE-CUDA: GPU-accelerated t-SNE and its applications to modern data. arXiv 2018;1807.11824.
  6. Belkina AC, Ciccolella CO, Anno R, Halpert R, Spidlen J, Snyder-Cappione JE. Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nat Commun 2019;10:5415.
  7. McInnes L, Healy J, Melville J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv 2018;1802.03426.
  8. Johnson J, Douze M, Jegou H. Billion-scale similarity search with GPUs. IEEE Trans Big Data 2021;7:535-547.
  9. RAPIDS Development Team. RAPIDS cuVS: GPU-accelerated vector search and clustering [software]. Available from: https://github.com/rapidsai/cuvs.
  10. Melville J. uwot: The Uniform Manifold Approximation and Projection method for dimensionality reduction [software]. Available from: https://github.com/jlmelville/uwot.
  11. Krijthe JH. Rtsne: T-distributed stochastic neighbor embedding using Barnes-Hut implementation [software]. Available from: https://github.com/jkrijthe/Rtsne.
  12. amine m. AppleSiliconFFT: FFT kernels for Apple Silicon GPUs [software]. Available from: https://github.com/aminems/AppleSiliconFFT.
  13. McInnes L, Healy J, Saul N, Grossberger L. UMAP: Uniform Manifold Approximation and Projection. J Open Source Softw 2018;3:861.
  14. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech 2008;2008:P10008.
  15. Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 2019;9:5233.
  16. Pons P, Latapy M. Computing communities in large networks using random walks. J Graph Algorithms Appl 2006;10:191-218.
  17. Staudt CL, Sazonovs A, Meyerhenke H. NetworKit: a tool suite for large-scale complex network analysis. Network Sci 2016;4:508-530.

Software Provenance Notes

  • uwot and Rtsne are benchmark/reference implementations only. Their source code is not vendored into fastEmbedR.
  • fastEmbedR contains the compact native CPU/Metal and optional direct FAISS/cuVS CUDA KNN routes needed by one-call embeddings. The CUDA adapter was distilled from the pinned faissR commit recorded in inst/COPYRIGHTS, but fastEmbedR does not invoke that R package.
  • openTSNE, FIt-SNE, t-SNE-CUDA, and opt-SNE informed the t-SNE optimization design. The public package implementation is native C++/Metal/CUDA code.
  • AppleSiliconFFT informed the native Metal FFT-grid engineering. The package keeps its own backend surface and does not expose experimental FFT variants.
  • Louvain, Leiden, and Walktrap are native package implementations [14-16]. The Leiden phase organization was informed by MIT-licensed NetworKit [17]; fastEmbedR does not link NetworKit, igraph, or cuGraph at run time.