SPATIALLY AWARE ENSEMBLE-BASED LEARNING TO PREDICT WEATHER-RELATED OUTAGES IN TRANSMISSION
Dokic, T., et. al. @ HICSS 2018
This paper describes the implementation of a prediction model for real-time assessment of weather-related
MODELING MOBILE USER ACTIONS FOR PURCHASE RECOMMENDATIONS USING DEEP MEMORY NETWORKS
Gligorijevic, Dj., et. al. @ SIGIR 2018
The rapid expansion of mobile devices has brought an unprecedented opportunity for mobile operators
DEEP ATTENTION MODEL FOR TRIAGE OF EMERGENCY DEPARTMENT PATIENTS
Gligorijevic, Dj., Stojanovic, J., et. al. @ SDM 2018
Optimization of patient throughput and wait time in emergency departments (ED) is an important task for
MODELING CUSTOMER ENGAGEMENT FROM PARTIAL OBSERVATIONS
Stojanovic, J., et. al. @ CIKM 2016
It is of high interest for a company to identify customers expected to bring the largest profit in the
UNCERTAINTY PROPAGATION IN LONG-TERM STRUCTURED REGRESSION ON EVOLVING NETWORKS
Gligorijevic, Dj., et. al. @ AAAI 2016
In long-term forecasting, it is important to estimate the confidence of predictions, as they are often affected
IMPROVING CONFIDENCE WHILE PREDICTING TRENDS IN TEMPORAL DISEASE NETWORKS
Gligorijevic, Dj., et. al. @ SDM-WDMMH 2015
For highly sensitive real-world predictive analytic applications such as healthcare and medicine, having
SEMI-SUPERVISED LEARNING FOR STRUCTURED REGRESSION ON PARTIALLY OBSERVED ATTRIBUTED GRAPHS
Stojanovic, J., et. al. @ SDM 2015
Conditional probabilistic graphical models provide a powerful framework for structured regression in