To tackle the high inference latency exhibited by autoregressive language models, previous
studies have proposed an early-exiting framework that allocates adaptive computation paths …

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two
fundamental invertible transformations, namely, serial nonlinear transformation and parallel …

Fog computing is an encouraging technology in the coming generation to pipeline the
breach between cloud data centers and Internet of Things (IoT) devices. Fog computing is …

A novel Bayesian matrix factorization method for bounded support data is presented. Each
entry in the observation matrix is assumed to be beta distributed. As the beta distribution has …

Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural
networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate …

In this paper, we focus on the variational learning of finite Dirichlet mixture models.
Compared to other algorithms that are commonly used for mixture models (such as …

Deep learning technology can fit the nonlinear relations between different logging
sequences. It solves the prediction problems that cannot be effectively disposed by …

Human mobility behavior is far from random, and its indicators follow non-Gaussian
distributions. Predicting human mobility has the potential to enhance location-based …

In this paper, the Gaussian mixture model (GMM) is introduced to implement channel
multipath clustering. The GMM incorporates the covariance structure and the mean …

Real-world data inevitably contains noisy labels, which induce the poor generalization of
deep neural networks. It is known that the network typically begins to rapidly memorize false …