The paper investigates how a Quantum Convolutional Neural Network (QCNN) can classify complex bank policy documents, compares it with a classical TextCNN on 50‑ and 100‑sample scenarios, and shows QCNN achieves up to 7‑12% higher accuracy and F1 scores, reducing compliance risk.
This article introduces a novel few‑shot learning approach tailored for multi‑new‑class scenarios, discusses its background, problem definition, proposed parallel training framework, hierarchical fine‑tuning method, and presents extensive experiments demonstrating superior performance and computational efficiency.
The paper presents Didi’s three‑layer governance‑algorithm framework for O2O ride‑hailing, addressing high business complexity, limited labeled data, interpretability, and multimodal features through small‑sample, transfer, and multi‑task learning, achieving notable gains in dispute resolution, NPS and CPO while highlighting remaining data and robustness challenges.
