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08/08/2023
Quantization Aware Factorization for Deep Neural Network Compression
Tensor decomposition of convolutional and fully-connected layers is an e...
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06/19/2023
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation
In this paper, the problem of image super-resolution for Optical Coheren...
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06/16/2023
Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition
Pedestrian Attribute Recognition (PAR) deals with the problem of identif...
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05/16/2023
Image Reconstruction using Superpixel Clustering and Tensor Completion
This paper presents a pixel selection method for compact image represent...
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05/08/2023
Adaptive Cross Tubal Tensor Approximation
In this paper, we propose a new adaptive cross algorithm for computing a...
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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...
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07/13/2022
Cross Tensor Approximation for Image and Video Completion
This paper proposes a general framework to use the cross tensor approxim...
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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...
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03/05/2022
How to Train Unstable Looped Tensor Network
A rising problem in the compression of Deep Neural Networks is how to re...
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08/21/2021
L3C-Stereo: Lossless Compression for Stereo Images
A large number of autonomous driving tasks need high-definition stereo i...
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03/15/2021
Meta-Solver for Neural Ordinary Differential Equations
A conventional approach to train neural ordinary differential equations ...
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12/12/2020
Improving EEG Decoding via Clustering-based Multi-task Feature Learning
Accurate electroencephalogram (EEG) pattern decoding for specific mental...
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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...
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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...
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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...
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04/20/2020
Towards Understanding Normalization in Neural ODEs
Normalization is an important and vastly investigated technique in deep ...
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03/11/2020
Interpolated Adjoint Method for Neural ODEs
In this paper, we propose a method, which allows us to alleviate or comp...
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02/26/2020
Using Reinforcement Learning in the Algorithmic Trading Problem
The development of reinforced learning methods has extended application ...
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02/25/2020
Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting
This work proposes a novel approach for multiple time series forecasting...
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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...
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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...
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10/15/2019
Reduced-Order Modeling of Deep Neural Networks
We introduce a new method for speeding up the inference of deep neural n...
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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...
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07/30/2019
Multi-Kernel Capsule Network for Schizophrenia Identification
Objective: Schizophrenia seriously affects the quality of life. To date,...
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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 ...
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09/03/2018
Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition
The Canonical Polyadic decomposition (CPD) is a convenient and intuitive...
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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...
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09/25/2017
Error Preserving Correction for CPD and Bounded-Norm CPD
In CANDECOMP/PARAFAC tensor decomposition, degeneracy often occurs in so...
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09/25/2017
Best Rank-One Tensor Approximation and Parallel Update Algorithm for CPD
A novel algorithm is proposed for CANDECOMP/PARAFAC tensor decomposition...
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05/20/2017
Learning Efficient Tensor Representations with Ring Structure Networks
Tensor train (TT) decomposition is a powerful representation for high-or...
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06/17/2016
Tensor Ring Decomposition
Tensor networks have in recent years emerged as the powerful tools for s...
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05/10/2015
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion
Tucker decomposition is the cornerstone of modern machine learning on te...
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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...
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10/23/2014
Canonical Polyadic Decomposition with Auxiliary Information for Brain Computer Interface
Physiological signals are often organized in the form of multiple dimens...
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10/09/2014
Bayesian Robust Tensor Factorization for Incomplete Multiway Data
We propose a generative model for robust tensor factorization in the pre...
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10/03/2014
Feature Learning from Incomplete EEG with Denoising Autoencoder
An alternative pathway for the human brain to communicate with the outsi...
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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...
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04/17/2014
Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness
Nonnegative Tucker decomposition (NTD) is a powerful tool for the extrac...
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01/25/2014
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powe...
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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...
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05/02/2013
Tensor Decompositions: A New Concept in Brain Data Analysis?
Matrix factorizations and their extensions to tensor factorizations and ...
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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...
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07/05/2012
