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

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/

Modern deep learning approaches haveshown promising results inmeteorological applications like precipitation nowcasting, synthetic radar generation, front detection and several others. Inorder toeffectively train and validate these complex algorithms, large and diverse datasets containing high-resolution imagery are required. Petabytes of weather data, such as from the Geostationary Environmental SatelliteSystem(GOES)andtheNext-Generation Radar(NEXRAD) system, are available to the public; however, the size and complexity of these datasets isahindrance todeveloping and training deep models.

'Scale the model, scale the data, scale the compute' is the reigning sentiment in the world of generative AI today.

Video-quality measurement is a critical task in video processing.

As large language models gain widespread adoption, running them efficiently becomesacrucialtask.