4 citations · 7 across the 3 of their papers we have counts for
4 papers · 1 filter
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
Ronilo J. Ragodos, Tong Wang, Qihang Lin +1
While deep reinforcement learning has proven to be successful in solving control tasks, the "black-box" nature of an agent has received increasing concerns. We propose a prototype-…
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
Amin Vahedian, Xun Zhou, Ling Tong +2
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigatin…
Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer Survival Curves in Iowa
Michael T. Lash, Min Zhang, Xun Zhou +2
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore differe…
Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Michael T. Lash, Yuqi Sun, Xun Zhou +2
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the us…