Andrzej Cichocki

research

∙ 05/16/2023

Image Reconstruction using Superpixel Clustering and Tensor Completion

This paper presents a pixel selection method for compact image represent...

0 Maame G. Asante-Mensah, et al. ∙

research

∙ 05/08/2023

Adaptive Cross Tubal Tensor Approximation

In this paper, we propose a new adaptive cross algorithm for computing a...

0 Salman Ahmadi Asl, et al. ∙

research

∙ 05/01/2023

Robust Low-Tubal-rank tensor recovery Using Discrete Empirical Interpolation Method with Optimized Slice/Feature Selection

In this paper, we extend the Discrete Empirical Interpolation Method (DE...

0 Salman Ahmadi Asl, et al. ∙

research

∙ 07/13/2022

Cross Tensor Approximation for Image and Video Completion

This paper proposes a general framework to use the cross tensor approxim...

0 Salman Ahmadi Asl, et al. ∙

research

∙ 04/30/2022

TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning

We present a novel procedure for optimization based on the combination o...

0 Konstantin Sozykin, et al. ∙

research

∙ 03/05/2022

How to Train Unstable Looped Tensor Network

A rising problem in the compression of Deep Neural Networks is how to re...

3 Anh-Huy Phan, et al. ∙

research

∙ 08/21/2021

L3C-Stereo: Lossless Compression for Stereo Images

A large number of autonomous driving tasks need high-definition stereo i...

0 Zihao Huang, et al. ∙

research

∙ 03/15/2021

Meta-Solver for Neural Ordinary Differential Equations

A conventional approach to train neural ordinary differential equations ...

0 Julia Gusak, et al. ∙

research

∙ 12/12/2020

Improving EEG Decoding via Clustering-based Multi-task Feature Learning

Accurate electroencephalogram (EEG) pattern decoding for specific mental...

0 Yu Zhang, et al. ∙

research

∙ 11/22/2020

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Deep learning has achieved excellent performance in a wide range of doma...

0 Shu Gong, et al. ∙

research

∙ 08/12/2020

Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network

Most state of the art deep neural networks are overparameterized and exh...

7 Anh-Huy Phan, et al. ∙

research

∙ 08/07/2020

On Multiple Intelligences and Learning Styles for Multi- Agent Systems: Future Research Trends in AI with a Human Face?

This article discusses recent trends and concepts in developing new kind...

0 Andrzej Cichocki, et al. ∙

research

∙ 04/20/2020

Towards Understanding Normalization in Neural ODEs

Normalization is an important and vastly investigated technique in deep ...

2 Julia Gusak, et al. ∙

research

∙ 03/11/2020

Interpolated Adjoint Method for Neural ODEs

In this paper, we propose a method, which allows us to alleviate or comp...

12 Talgat Daulbaev, et al. ∙

research

∙ 02/26/2020

Using Reinforcement Learning in the Algorithmic Trading Problem

The development of reinforced learning methods has extended application ...

34 Evgeny Ponomarev, et al. ∙

research

∙ 02/25/2020

Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting

This work proposes a novel approach for multiple time series forecasting...

0 Qiquan Shi, et al. ∙

research

∙ 01/20/2020

Randomized Algorithms for Computation of Tucker decomposition and Higher Order SVD (HOSVD)

Big data analysis has become a crucial part of new emerging technologies...

0 Salman Ahmadi Asl, et al. ∙

research

∙ 12/20/2019

MERACLE: Constructive layer-wise conversion of a Tensor Train into a MERA

In this article two new algorithms are presented that convert a given da...

0 Kim Batselier, et al. ∙

research

∙ 10/15/2019

Reduced-Order Modeling of Deep Neural Networks

We introduce a new method for speeding up the inference of deep neural n...

0 Talgat Daulbaev, et al. ∙

research

∙ 08/08/2019

Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"

Deep image prior (DIP), which utilizes a deep convolutional network (Con...

18 Tatsuya Yokota, et al. ∙

research

∙ 07/30/2019

Multi-Kernel Capsule Network for Schizophrenia Identification

Objective: Schizophrenia seriously affects the quality of life. To date,...

4 Tian Wang, et al. ∙

research

∙ 03/24/2019

One time is not enough: iterative tensor decomposition for neural network compression

The low-rank tensor approximation is very promising for the compression ...

24 Julia Gusak, et al. ∙

research

∙ 09/03/2018

Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition

The Canonical Polyadic decomposition (CPD) is a convenient and intuitive...

0 Anh-Huy Phan, et al. ∙

research

∙ 03/20/2018

Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization

Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of produci...

0 Jinshi Yu, et al. ∙

research

∙ 09/25/2017

Error Preserving Correction for CPD and Bounded-Norm CPD

In CANDECOMP/PARAFAC tensor decomposition, degeneracy often occurs in so...

0 Anh-Huy Phan, et al. ∙

research

∙ 09/25/2017

Best Rank-One Tensor Approximation and Parallel Update Algorithm for CPD

A novel algorithm is proposed for CANDECOMP/PARAFAC tensor decomposition...

0 Anh-Huy Phan, et al. ∙

research

∙ 05/20/2017

Learning Efficient Tensor Representations with Ring Structure Networks

Tensor train (TT) decomposition is a powerful representation for high-or...

0 Qibin Zhao, et al. ∙

research

∙ 06/17/2016

Tensor Ring Decomposition

Tensor networks have in recent years emerged as the powerful tools for s...

0 Qibin Zhao, et al. ∙

research

∙ 05/10/2015

Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion

Tucker decomposition is the cornerstone of modern machine learning on te...

0 Qibin Zhao, et al. ∙

research

∙ 03/06/2015

Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements

Background subtraction has been a fundamental and widely studied task in...

0 Wenfei Cao, et al. ∙

research

∙ 10/23/2014

Canonical Polyadic Decomposition with Auxiliary Information for Brain Computer Interface

Physiological signals are often organized in the form of multiple dimens...

0 Junhua Li, et al. ∙

research

∙ 10/09/2014

Bayesian Robust Tensor Factorization for Incomplete Multiway Data

We propose a generative model for robust tensor factorization in the pre...

0 Qibin Zhao, et al. ∙

research

∙ 10/03/2014

Feature Learning from Incomplete EEG with Denoising Autoencoder

An alternative pathway for the human brain to communicate with the outsi...

0 Junhua Li, et al. ∙

research

∙ 09/01/2014

Multi-tensor Completion for Estimating Missing Values in Video Data

Many tensor-based data completion methods aim to solve image and video i...

0 Chao Li, et al. ∙

research

∙ 04/17/2014

Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness

Nonnegative Tucker decomposition (NTD) is a powerful tool for the extrac...

0 Guoxu Zhou, et al. ∙

research

∙ 01/25/2014

Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powe...

0 Qibin Zhao, et al. ∙

research

∙ 08/26/2013

Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis

Canonical correlation analysis (CCA) has been one of the most popular me...

0 Yu Zhang, et al. ∙

research

∙ 05/02/2013

Tensor Decompositions: A New Concept in Brain Data Analysis?

Matrix factorizations and their extensions to tensor factorizations and ...

0 Andrzej Cichocki, et al. ∙

research

∙ 12/17/2012

Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction

Very often data we encounter in practice is a collection of matrices rat...

0 Guoxu Zhou, et al. ∙

research

∙ 07/05/2012

Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method

A new generalized multilinear regression model, termed the Higher-Order ...

0 Qibin Zhao, et al. ∙

Andrzej Cichocki

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