Although the latest high-end smartphone has powerful CPU and GPU, running deeper
convolutional neural networks (CNNs) for complex tasks such as ImageNet classification on …

Modern neural networks have revolutionized the fields of computer vision (CV) and Natural
Language Processing (NLP). They are widely used for solving complex CV tasks and NLP …

Abstract Knowledge distillation is to transfer the knowledge of a large neural network into a
smaller one and has been shown to be effective especially when the amount of training data …

Network compression reduces the computational complexity and memory consumption of
deep neural networks by reducing the number of parameters. In SVD-based network …

A central task of Bayesian machine learning is to infer the posterior distribution of hidden
random variables given observations and calculate expectations with respect to this …

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image
and Video Processing shows you how robust subspace learning and tracking by …

This work identifies the existence and cause of a type of posterior collapse that frequently
occurs in the Bayesian deep learning practice. For a general linear latent variable model …

Genetic association studies have yielded a wealth of biological discoveries. However, these
studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the …

Recently, convolutional neural network (CNN) compression via low-rank decomposition has
achieved remarkable performance. Finding the optimal rank is a crucial problem because …

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as
an essential yet challenging problem. In particular, since thetensor rank controls the …