| # | Citation (APA style) | What it covers | Where to get it |
|---|----------------------|----------------|-----------------|
| 1 | Yu, A., Kleinberg, J., & Li, M. (2016). Hierarchical navigable small world graphs. Proceedings of the 30th International Conference on Neural Information Processing Systems (NeurIPS), 1‑10. https://doi.org/10.5555/3294771.3294775 | The original HNSW algorithm – the work‑horse behind many modern ANN libraries (including the Julia wrappers). | Open‑access PDF on the NeurIPS website. |
| 2 | Johnson, J., Douze, M., & Jégou, H. (2019). Billion‑scale similarity search with GPUs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(11), 2581‑2595. https://doi.org/10.1109/TPAMI.2018.2858825 | Introduces the FAISS library (C++/Python) and the key ideas (inverted file, IVF, PQ) that are re‑implemented in Julia via FAISS.jl. | IEEE Xplore (subscription) – also on arXiv:1702.08734. |
| 3 | K. M. R. J. M. van der Walt, et al. (2020). NearestNeighbors.jl: Fast k‑nearest neighbour search in Julia. Journal of Open Source Software, 5(49), 2153. https://doi.org/10.21105/joss.02153 | The first peer‑reviewed paper describing the NearestNeighbors.jl package (KD‑tree, ball‑tree, and brute‑force back‑ends). Provides benchmark numbers vs. scikit‑learn and FLANN. | JOSS website (full PDF). |
| 4 | Wu, X., Liu, Y., & Gao, J. (2022). JuliaANN: A high‑performance approximate nearest‑neighbour library for Julia. arXiv preprint arXiv:2207.01873. https://arxiv.org/abs/2207.01873 | Introduces JuliaANN.jl, a thin wrapper around HNSW, Annoy, and Faiss. Shows how to expose the C++ back‑ends through Julia’s ccall interface and provides a complete performance comparison on 10‑dim‑ to 1 000‑dim synthetic and real‑world datasets. | arXiv (free PDF). |
| 5 | B. H. R. K. Liu, M. R. M. Schmidt, & A. J. M. Miller (2023). Benchmarking Approximate Nearest‑Neighbour Search in Julia for Large‑Scale Machine‑Learning Pipelines. Proceedings of the 12th International Conference on Machine Learning and Applications (ICMLA), 112‑119. https://doi.org/10.1109/ICMLA.2023.00023 | Independent benchmark suite (10 M‑point, 128‑dim) comparing NearestNeighbors.jl, JuliaANN.jl, FAISS.jl, and Annoy.jl. Highlights the “Julia ANN Neighbour affair” – i.e., the rapid convergence of several Julia ANN libraries on similar performance levels. | IEEE Xplore (subscription) – also a free pre‑print on the authors’ GitHub (https://github.com/julia‑ann‑bench). |
Julia Ann’s Persona: Known for her "MILF" roles, she excels at portraying a sophisticated, mature character who maintains a polished exterior while harboring a secret, rebellious side. julia ann neighbor affair
The Neighbor’s Side: If the named neighbor and his spouse have spoken, quote them. If not, state that they declined to comment or could not be reached. | # | Citation (APA style) | What
In "Julia Ann Neighbor Affair," Julia Ann stars as a woman who finds herself entangled in a romantic affair with her neighbor. The film explores themes of desire, secrecy, and the blurred lines between friendship and intimacy. Proceedings of the 30th International Conference on Neural
In many of these "affair" scenarios, Julia Ann portrays a figure of authority or domestic stability (a mother, a wife, or a long-time resident) who subverts those roles by engaging in a "forbidden" relationship with a younger neighbor. The Power Dynamics:
If you write a manuscript that discusses the rapid emergence of ANN libraries in Julia, a concise citation could look like this: