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MERACLE: Constructive layer-wise conversion of a Tensor Train into a MERA
Kim Batselier, Andrzej Cichocki, Ngai Wong
In this article two new algorithms are presented that convert a given data tensor train into either a Tucker decomposition with orthogonal matrix factors or a multi-scale entanglem…
Reduced-Order Modeling of Deep Neural Networks
Julia Gusak, Talgat Daulbaev, Evgeny Ponomarev +2
We introduce a new method for speeding up the inference of deep neural networks. It is somewhat inspired by the reduced-order modeling techniques for dynamical systems.The cornerst…
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image Prior
Tatsuya Yokota, Hidekata Hontani, Qibin Zhao +1
Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attracted attentions in computer vision and machine learning c…
Multi-Kernel Capsule Network for Schizophrenia Identification
Tian Wang, Anastasios Bezerianos, Andrzej Cichocki +1
Objective: Schizophrenia seriously affects the quality of life. To date, both simple (linear discriminant analysis) and complex (deep neural network) machine learning methods have…
MUSCO: Multi-Stage Compression of neural networks
Julia Gusak, Maksym Kholiavchenko, Evgeny Ponomarev +3
The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank…