2 citations · 3 across the 6 of their papers we have counts for
7 papers
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…
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…
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…
Transportation Interventions Reshaping NYC Commute: the Probabilistic Simulation Framework Assessing the Impacts of Ridesharing and Manhattan Congestion Surcharge
Devashish Khulbe, Chaogui Kang, Stanislav Sobolevsky
Understanding holistic impact of planned transportation solutions and interventions on urban systems is challenged by their complexity but critical for decision making. The corners…
Deep Learning Based Android Malware Detection Framework
Soumya Sourav, Devashish Khulbe, Naman Kapoor
With the development in the field of smartphones and ever growing base of Internet, various softwares are left prone to many malicious activities like pharming, phishing, ransomwar…
Modeling Severe Traffic Accidents With Spatial And Temporal Features
Devashish Khulbe, Soumya Sourav
We present an approach to estimate the severity of traffic related accidents in aggregated (area-level) and disaggregated (point level) data. Exploring spatial features, we measure…