36 citations · 44 across the 6 of their papers we have counts for
6 papers
Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates
Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang +2
Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of ext…
Engineering an algorithm for constructing low-stretch geometric graphs with near-greedy average-degrees
FNU Shariful, Justin Weathers, Anirban Ghosh +1
We design and engineer Fast-Sparse-Spanner, a simple and practical (fast and memory-efficient) algorithm for constructing sparse low stretch-factor geometric graphs on large points…
Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting
Jimeng Shi, Rukmangadh Myana, Vitalii Stebliankin +2
Accurate time series forecasting is a fundamental challenge in data science. It is often affected by external covariates such as weather or human intervention, which in many applic…
Time Series Forecasting (TSF) Using Various Deep Learning Models
Jimeng Shi, Mahek Jain, Giri Narasimhan
Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learnin…
Cache Replacement as a MAB with Delayed Feedback and Decaying Costs
Farzana Beente Yusuf, Vitalii Stebliankin, Giuseppe Vietri +1
Inspired by the cache replacement problem, we propose and solve a new variant of the well-known multi-armed bandit (MAB), thus providing a solution for improving existing state-of-…
Analyzing Adaptive Cache Replacement Strategies
Mario E. Consuegra, Wendy A. Martinez, Giri Narasimhan +3
Adaptive Replacement Cache (ARC) and CLOCK with Adaptive Replacement (CAR) are state-of-the- art "adaptive" cache replacement algorithms invented to improve on the shortcomings of…