
MetaSolver for Neural Ordinary Differential Equations
A conventional approach to train neural ordinary differential equations ...
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Improving EEG Decoding via Clusteringbased Multitask Feature Learning
Accurate electroencephalogram (EEG) pattern decoding for specific mental...
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Deep Learning in EEG: Advance of the Last TenYear Critical Period
Deep learning has achieved excellent performance in a wide range of doma...
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Stable Lowrank Tensor Decomposition for Compression of Convolutional Neural Network
Most state of the art deep neural networks are overparameterized and exh...
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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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Towards Understanding Normalization in Neural ODEs
Normalization is an important and vastly investigated technique in deep ...
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Interpolated Adjoint Method for Neural ODEs
In this paper, we propose a method, which allows us to alleviate or comp...
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Using Reinforcement Learning in the Algorithmic Trading Problem
The development of reinforced learning methods has extended application ...
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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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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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MERACLE: Constructive layerwise 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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ReducedOrder Modeling of Deep Neural Networks
We introduce a new method for speeding up the inference of deep neural n...
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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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MultiKernel Capsule Network for Schizophrenia Identification
Objective: Schizophrenia seriously affects the quality of life. To date,...
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One time is not enough: iterative tensor decomposition for neural network compression
The lowrank tensor approximation is very promising for the compression ...
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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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Learning the Hierarchical Parts of Objects by Deep NonSmooth Nonnegative Matrix Factorization
Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of produci...
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Error Preserving Correction for CPD and BoundedNorm CPD
In CANDECOMP/PARAFAC tensor decomposition, degeneracy often occurs in so...
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Best RankOne Tensor Approximation and Parallel Update Algorithm for CPD
A novel algorithm is proposed for CANDECOMP/PARAFAC tensor decomposition...
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Learning Efficient Tensor Representations with Ring Structure Networks
Tensor train (TT) decomposition is a powerful representation for highor...
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Tensor Ring Decomposition
Tensor networks have in recent years emerged as the powerful tools for s...
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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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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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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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Bayesian Robust Tensor Factorization for Incomplete Multiway Data
We propose a generative model for robust tensor factorization in the pre...
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Feature Learning from Incomplete EEG with Denoising Autoencoder
An alternative pathway for the human brain to communicate with the outsi...
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Multitensor Completion for Estimating Missing Values in Video Data
Many tensorbased data completion methods aim to solve image and video i...
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Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness
Nonnegative Tucker decomposition (NTD) is a powerful tool for the extrac...
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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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Frequency Recognition in SSVEPbased BCI using Multiset Canonical Correlation Analysis
Canonical correlation analysis (CCA) has been one of the most popular me...
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Tensor Decompositions: A New Concept in Brain Data Analysis?
Matrix factorizations and their extensions to tensor factorizations and ...
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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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HigherOrder Partial Least Squares (HOPLS): A Generalized MultiLinear Regression Method
A new generalized multilinear regression model, termed the HigherOrder ...
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Andrzej Cichocki
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Senior team leader and the head of the Cichocki laboratory for Advanced Brain Signal Processing, at RIKEN Brain Science Institute in Japan.