PDI-Net integrates a semi-U-Net encoder with YOLO detection using a physics-aware PALS-Bridge and optical simulation to deliver 84% faster inference and 5% higher mAP than pruned reconstruction-plus-detection on low-SNR M3FD infrared data.
Efficient neural architecture search via parameters sharing
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Bi-NAS applies bi-level NAS to search explanation architectures and LLMs for text generation, reporting gains in both recommendation accuracy and explanation effectiveness across four real-world datasets.
citing papers explorer
-
Dual-Integrated Low-Latency Single-Lens Infrared Computational Imaging for Object Detection
PDI-Net integrates a semi-U-Net encoder with YOLO detection using a physics-aware PALS-Bridge and optical simulation to deliver 84% faster inference and 5% higher mAP than pruned reconstruction-plus-detection on low-SNR M3FD infrared data.
-
Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search
Bi-NAS applies bi-level NAS to search explanation architectures and LLMs for text generation, reporting gains in both recommendation accuracy and explanation effectiveness across four real-world datasets.