Neighbor Search

Neighbor search aims to efficiently identify data points closest to a given query point, a fundamental problem across numerous fields. Current research focuses on improving the speed and accuracy of these searches, particularly for high-dimensional data, exploring techniques like adaptive group testing, ray tracing acceleration (especially for unbounded searches), and approximate nearest neighbor methods tailored to specific decoder architectures (e.g., HadamardMLP). These advancements are crucial for scaling applications like plagiarism detection, link prediction in large graphs, and robust image retrieval, where efficient and accurate neighbor identification is paramount.