As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

摘要网络大数据是指“人, 机, 物” 三元世界在网络空间(Cyberspace) 中交互,
融合所产生并在互联网上可获得的的大数据. 网络大数据的规模和复杂度的增长超出了硬件能力 …

Context. The chemo-physical parametrisation of stellar spectra is essential for
understanding the nature and evolution of stars and of Galactic stellar populations. A …

This tutorial introduces the reader to Gaussian process regression as an expressive tool to
model, actively explore and exploit unknown functions. Gaussian process regression is a …

In natural behaviour, animals actively interrogate their environments using endogenously
generated 'question-and-answer'strategies. However, in laboratory settings participants …

A central problem in machine learning involves modeling complex data-sets using highly
flexible families of probability distributions in which learning, sampling, inference, and …

We develop stochastic variational inference, a scalable algorithm for approximating
posterior distributions. We develop this technique for a large class of probabilistic models …

Understanding information processing in the brain—and creating general-purpose artificial
intelligence—are long-standing aspirations of scientists and engineers worldwide. The …

Soil erosion is a serious threat to sustainable agriculture, food production, and
environmental security. The advancement of accurate models for soil erosion susceptibility …