The Big Data revolution promises to transform how we live, work, and think by enabling
process optimization, empowering insight discovery and improving decision making. The …

Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …

Federated learning still faces many problems from research to technology implementation
and the most critical problem is that the communication efficiency is not high. Therefore, the …

Ranking in information retrieval has been traditionally approached as a pursuit of relevant
information, under the assumption that the users' information needs are unambiguously …

Pancreatic cancer has the worst prognosis of all cancers. The clinical application of
endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep …

Medical imaging datasets usually exhibit domain shift due to the variations of scanner
vendors, imaging protocols, etc. This raises the concern about the generalization capacity of …

We demonstrate that a production-quality keyword-spotting model can be trained on-device
using federated learning and achieve comparable false accept and false reject rates to a …

Most of the classic theoretical systems and tools in statistics, data mining and machine
learning are built on the fundamental assumption of IIDness, which assumes the …

The importance of processing stream data increases with new technologies and new use
cases. Applying machine learning to stream data and process them in real time leads to …

Given the wide adoption of multimodal sensors (eg, camera, lidar, radar) by autonomous
vehicle s (AVs), deep analytics to fuse their outputs for a robust perception become …