This paper proposes a scalable technique for develo** lightweight yet powerful models for
object detection in videos using self-training with knowledge distillation. This approach …

The proliferation of automated data collection schemes and the advances in sensorics are
increasing the amount of data we are able to monitor in real-time. However, given the high …

In many industrial applications, obtaining labeled observations is not straightforward as it
often requires the intervention of human experts or the use of expensive testing equipment …

Online active learning is a paradigm in machine learning that aims to select the most
informative data points to label from a data stream. The problem of minimizing the cost …

High-quality corpora have been very scarce for the text emotion research. Existing corpora
with multi-label emotion annotations have been either too small or too class-biased to …

Active regression considers a linear regression problem where the learner receives a large
number of data points but can only observe a small number of labels. Since online …

In data stream modeling problems, online active learning plays an important role for
reducing model update times and costs (or efforts) associated with measuring and collecting …

We discuss planning methods for bike-sharing systems that operate a set of stations
consisting of docks. Specific questions include decisions related to the number of docks to …

We propose an approach for exploiting machine learning to approximate electronic fields in
crystalline solids subjected to deformation. Strain engineering is emerging as a widely used …

As bike-share systems expand in urban areas, the wealth of publicly available data has
drawn researchers to address the novel operational challenges these systems face. One key …