Thus, instead of simply detecting changed pixels, we want to identify change events. We define a change event as a group of pixels over space and time that are all changed by a single event. Weareinterested indeveloping systems thatcanautomatically detectchangeeventsandassign to each a semantic label that indicates the nature of the event, e.g., forest fires, road construction etc. Identifying change events is a much more challenging problem than change detection.

Neural Information Processing Systems http://nips.cc/

Tensor decomposition is an important tool for multiway data analysis.

Inthispaper,wepropose a tractable algorithm that estimates the probabilistic aggregation map from the system'strajectory.

A recently proposed class of models attempts to learn latent dynamics from high-dimensionalobservations,likeimages,usingpriorsinformedbyHamiltonian mechanics.

We refer to it as theContinuous Category Discovery(CCD) problem, which is significantly more challenging than the static setting.

The algorithm requires solving a non-convex optimization problem inthespace ofprobability measures, thatrequires delicate analysis.

A challenge in mobile sensing is to overcome the location uncertainty of its sensors.