24 citations · 47 across the 14 of their papers we have counts for
5 papers · 1 filter
Event Detection in Noisy Streaming Data with Combination of Corroborative and Probabilistic Sources
Abhijit Suprem, Calton Pu
Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alterna…
Concept Drift Adaptive Physical Event Detection for Social Media Streams
Abhijit Suprem, Aibek Musaev, Calton Pu
Event detection has long been the domain of physical sensors operating in a static dataset assumption. The prevalence of social media and web access has led to the emergence of soc…
ASSED -- A Framework for Identifying Physical Events through Adaptive Social Sensor Data Filtering
Abhijit Suprem, Calton Pu
Physical event detection has long been the domain of static event processors operating on numeric sensor data. This works well for large scale strong-signal events such as hurrican…
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
Yanzhao Wu, Ling Liu, Juhyun Bae +6
Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep neural networks (DNNs). Even for the baseline of a constant learning rate, it is non-trivi…
Differentially Private Model Publishing for Deep Learning
Lei Yu, Ling Liu, Calton Pu +2
Deep learning techniques based on neural networks have shown significant success in a wide range of AI tasks. Large-scale training datasets are one of the critical factors for thei…