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Found 6 papers, 0 papers with code
General-Purpose Multi-Modal OOD Detection Framework
To reach this goal, we propose a general-purpose weakly-supervised OOD detection framework, called WOOD, that combines a binary classifier and a contrastive learning component to reap the benefits of both.
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes
New technologies and the availability of geospatial data have drawn attention to spatio-temporal biases present in society.
Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision
We present an architecture to boost the precision of existing information extraction systems.
Information Extraction with Character-level Neural Networks and Free Noisy Supervision
We present an architecture for information extraction from text that augments an existing parser with a character-level neural network.
Quantifying Urban Traffic Anomalies
Detecting and quantifying anomalies in urban traffic is critical for real-time alerting or re-routing in the short run and urban planning in the long run.
Predicting Ambulance Demand: Challenges and Methods
Predicting ambulance demand accurately at a fine resolution in time and space (e. g., every hour and 1 km$^2$) is critical for staff / fleet management and dynamic deployment.
