Filtering job posting data before LLM-assisted clustering and hierarchical labeling yields taxonomies with better AI skill coverage than unfiltered approaches.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
The WaveletInception-BiGRU network uses learnable wavelet packet transforms, 1D Inception-ResNet modules, and BiGRU layers to generate high-resolution, spatially mapped health profiles from variable-speed vibration data, outperforming prior methods on track stiffness and transition zone tasks.
citing papers explorer
-
Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings
Filtering job posting data before LLM-assisted clustering and hierarchical labeling yields taxonomies with better AI skill coverage than unfiltered approaches.
-
WaveletInception Networks for on-board Vibration-Based Infrastructure Health Monitoring
The WaveletInception-BiGRU network uses learnable wavelet packet transforms, 1D Inception-ResNet modules, and BiGRU layers to generate high-resolution, spatially mapped health profiles from variable-speed vibration data, outperforming prior methods on track stiffness and transition zone tasks.