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Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion
YasasSenarath, SaideepNannapaneni, HemantPurohit....
Published date-11/10/2020
TrafficAccidentDetection
The number of emergencies have increased over the years with the growth in urbanization. This pattern has overwhelmed the emergency services with limited resources and demands the optimization of response …
On the State of Social Media Data for Mental Health Research
KeithHarrigian, CarlosAguirre, MarkDredze....
Published date-11/10/2020
Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain, in terms of both …
Dynamic Embeddings for Interaction Prediction
ZekariasT.Kefato, SarunasGirdzijauskas, NasrullahSheikh....
Published date-11/10/2020
RecommendationSystems
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. While the last decade has seen an explosion of RSs aimed at …
Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation
EdaBayram, AlbertoGarcia-Duran, RobertWest....
Published date-11/10/2020
KnowledgeGraphCompletion, KnowledgeGraphs, LinkPrediction
The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are …
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning
HanyAbdulsamad, PeterNickl, PascalKlink....
Published date-11/10/2020
Probabilistic regression techniques in control and robotics applications have to fulfill different criteria of data-driven adaptability, computational efficiency, scalability to high dimensions, and the capacity to deal with different modalities …
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
ElliottGordon-Rodriguez, GabrielLoaiza-Ganem, GeoffPleiss....
Published date-11/10/2020
Modern deep learning is primarily an experimental science, in which empirical advances occasionally come at the expense of probabilistic rigor. Here we focus on one such example; namely the use …