In recent years, deep neural networks (DNNs) have received increased attention, have been
applied to different applications, and achieved dramatic accuracy improvements in many …

Deep neural networks (DNNs) have recently achieved great success in many visual
recognition tasks. However, existing deep neural network models are computationally …

Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Machine learning and data mining algorithms are becoming increasingly important in
analyzing large volume, multi-relational and multi--modal datasets, which are often …

We consider the task of building compact deep learning pipelines suitable for deploymenton
storage and power constrained mobile devices. We propose a uni-fied framework to learn a …

Understanding turbulence is key to our comprehension of many natural and technological
flow processes. At the heart of this phenomenon lies its intricate multiscale nature …

This paper presents a novel knowledge distillation based model compression framework
consisting of a student ensemble. It enables distillation of simultaneously learnt ensemble …

We present a brief survey on the modern tensor numerical methods for multidimensional
stationary and time-dependent partial differential equations (PDEs). The guiding principle of …

Cross Tensor Approximation (CTA) is a generalization of Cross/skeleton matrix and CUR
Matrix Approximation (CMA) and is a suitable tool for fast low-rank tensor approximation. It …

We propose a new algorithm for calculation of vibrational spectra of molecules using tensor
train decomposition. Under the assumption that eigenfunctions lie on a low-parametric …