Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …

Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Comparing probability distributions is a fundamental problem in data sciences. Simple
norms and divergences such as the total variation and the relative entropy only compare …

We consider the problem of detecting out-of-distribution images in neural networks. We
propose ODIN, a simple and effective method that does not require any change to a pre …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Generative moment matching network (GMMN) is a deep generative model that differs from
Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two …

Deep networks have been successfully applied to learn transferable features for adapting
models from a source domain to a different target domain. In this paper, we present joint …

Generative neural networks are probabilistic models that implement sampling using
feedforward neural networks: they take a random input vector and produce a sample from a …

Generative Adversarial Nets (GANs) represent an important milestone for effective
generative models, which has inspired numerous variants seemingly different from each …