Hashing Based

Hashing-based methods are increasingly used to address computational challenges in various domains by efficiently approximating complex calculations or compressing large datasets. Current research focuses on improving the accuracy and speed of these methods, particularly through adaptive and dynamic hashing schemes, optimized quantization techniques, and novel loss functions tailored to specific applications like nearest neighbor search and model counting. These advancements are impacting fields ranging from machine learning (e.g., embedding table compression, image retrieval) to bioinformatics (e.g., genome analysis) by enabling faster and more scalable solutions for large-scale data processing and analysis.