Showing results for sarah Vector
GitHub Repo
https://github.com/uxiatulia/vector_art
uxiatulia/vector_art
small sample of some vector artwork by sarah :D
GitHub Repo
https://github.com/sarahwrittenhouse-labs/sarahwrittenhouse-labs
sarahwrittenhouse-labs/sarahwrittenhouse-labs
Systems thinker, builder, and AI explorer. Designing scalable applications with a focus on AI systems—LLMs, RAG, vector databases, and intelligent workflows. Technical product + engineering background.
GitHub Repo
https://github.com/Planet-Source-Code/sarah-mathiason-add-two-vectors__1-8659
Planet-Source-Code/sarah-mathiason-add-two-vectors__1-8659
No repository description available.
GitHub Repo
https://github.com/MikhailMorgan/Original-RRN
MikhailMorgan/Original-RRN
By Xinzi He, Jia Guo, Xuzhe Zhang, Hanwen Bi, Sarah Gerard, David Kaczka, Amin Motahari, Eric Hoffman, Joseph Reinhardt, R. Graham Barr, Elsa Angelini, and Andrew Laine. Paper link: [arXiv] https://arxiv.org/pdf/2106.07608. Unsupervised learning-based medical image registration approaches have witnessed rapid development in recent years. We propose to revisit a commonly ignored while simple and well-established principle: recursive refinement of deformation vector fields across scales. We introduce a recursive refinement network (RRN) for unsupervised medical image registration, to extract multi-scale features, construct normalized local cost correlation volume and recursively refine volumetric deformation vector fields. RRN achieves state of the art performance for 3D registration of expiratory-inspiratory pairs of CT lung scans. On DirLab COPDGene dataset, RRN returns an average Target Registration Error (TRE) of 0.83 mm, which corresponds to a 13% error reduction from the best result presented in the leaderboard 4. In addition to comparison with conventional methods, RRN leads to 89% error reduction compared to deep-learning-based peer approaches.
GitHub Repo
https://github.com/nkunwar30/Support-Vector-Machine