3 papers
cs.LG2026
FastODT: A tree-based framework for efficient continual learning
Daniel Bretsko, Piotr Walas, Devashish Khulbe +3
Machine learning models deployed in real-world settings must operate under evolving data distributions and constrained computational resources. This challenge is particularly acute…
cs.SI2025
Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities
Devashish Khulbe, Stanislav Sobolevsky
Delineating areas within metropolitan regions stands as an important focus among urban researchers, shedding light on the urban perimeters shaped by evolving population dynamics. A…
cs.LG2025
Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models
Devashish Khulbe, Alexander Belyi, Stanislav Sobolevsky
Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks…