Borda Counting

Borda counting is a method for aggregating ranked preferences, finding applications in diverse fields ranging from voting systems to object counting in images and videos. Current research focuses on improving the accuracy and efficiency of Borda counting, particularly in scenarios with noisy or incomplete data, employing techniques like deep learning models (e.g., transformers, Siamese networks) and novel algorithms (e.g., those incorporating persistent homology or curriculum learning). These advancements enhance the robustness and scalability of Borda counting, leading to improved performance in various applications, including crowd analysis, knowledge graph embedding, and even quantum state preparation.