Search Results for author: Sergios Theodoridis

Found 22 papers, 5 papers with code

Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel

1 code implementation15 Sep 2023 Richard Cornelius Suwandi, Zhidi Lin, Feng Yin, Zhiguo Wang, Sergios Theodoridis

This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework to optimize the hyper-parameters.

Gaussian Processes

Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models

2 code implementations3 Sep 2023 Zhidi Lin, Juan Maroñas, Ying Li, Feng Yin, Sergios Theodoridis

The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems.

Gaussian Processes Variational Inference

Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners

no code implementations1 Jun 2023 Sarthak Yadav, Sergios Theodoridis, Lars Kai Hansen, Zheng-Hua Tan

In this work, we propose a Multi-Window Masked Autoencoder (MW-MAE) fitted with a novel Multi-Window Multi-Head Attention (MW-MHA) module that facilitates the modelling of local-global interactions in every decoder transformer block through attention heads of several distinct local and global windows.

Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling

no code implementations28 May 2022 Lei Cheng, Feng Yin, Sergios Theodoridis, Sotirios Chatzis, Tsung-Hui Chang

However, a come back of Bayesian methods is taking place that sheds new light on the design of deep neural networks, which also establish firm links with Bayesian models and inspire new paths for unsupervised learning, such as Bayesian tensor decomposition.

Gaussian Processes Tensor Decomposition +1

Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness

1 code implementation5 Dec 2021 Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis

This work explores the potency of stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA), against powerful (gradient-based) white-box and black-box adversarial attacks; we especially focus on Adversarial Training settings.

Adversarial Attack Adversarial Defense +2

Dialog speech sentiment classification for imbalanced datasets

no code implementations15 Sep 2021 Sergis Nicolaou, Lambros Mavrides, Georgina Tryfou, Kyriakos Tolias, Konstantinos Panousis, Sotirios Chatzis, Sergios Theodoridis

Speech is the most common way humans express their feelings, and sentiment analysis is the use of tools such as natural language processing and computational algorithms to identify the polarity of these feelings.

Classification Sentiment Analysis +1

Local Competition and Stochasticity for Adversarial Robustness in Deep Learning

no code implementations4 Jan 2021 Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, Sergios Theodoridis

The main operating principle of the introduced units lies on stochastic arguments, as the network performs posterior sampling over competing units to select the winner.

Adversarial Attack Adversarial Robustness

Early soft and flexible fusion of EEG and fMRI via tensor decompositions

no code implementations12 May 2020 Christos Chatzichristos, Eleftherios Kofidis, Lieven De Lathauwer, Sergios Theodoridis, Sabine Van Huffel

The fusion methods reported so far ignore the underlying multi-way nature of the data in at least one of the modalities and/or rely on very strong assumptions about the relation of the two datasets.

EEG Electroencephalogram (EEG)

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

no code implementations8 Mar 2020 Feng Yin, Zhidi Lin, Yue Xu, Qinglei Kong, Deshi Li, Sergios Theodoridis, Shuguang, Cui

In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods.

Federated Learning

Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series

no code implementations21 Apr 2019 Feng Yin, Lishuo Pan, Xinwei He, Tianshi Chen, Sergios Theodoridis, Zhi-Quan, Luo

Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications.

Gaussian Processes regression +2

Nonparametric Bayesian Deep Networks with Local Competition

1 code implementation19 May 2018 Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis

To this end, we revisit deep networks that comprise competing linear units, as opposed to nonlinear units that do not entail any form of (local) competition.

Bayesian Inference

Unsupervised learning of the brain connectivity dynamic using residual D-net

no code implementations20 Apr 2018 Youngjoo Seo, Manuel Morante, Yannis Kopsinis, Sergios Theodoridis

In this paper, we propose a novel unsupervised learning method to learn the brain dynamics using a deep learning architecture named residual D-net.

General Classification

Information Assisted Dictionary Learning for fMRI data analysis

1 code implementation5 Feb 2018 Manuel Morante, Yannis Kopsinis, Sergios Theodoridis, Athanassios Protopapas

The new method allows the incorporation of a priori knowledge associated both with the experimental design as well as with available brain Atlases.

Dictionary Learning Experimental Design

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

no code implementations23 Mar 2017 Pantelis Bouboulis, Symeon Chouvardas, Sergios Theodoridis

To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented.

Assisted Dictionary Learning for fMRI Data Analysis

no code implementations11 Oct 2016 Manuel Morante Moreno, Yannis Kopsinis, Eleftherios Kofidis, Christos Chatzichristos, Sergios Theodoridis

Extracting information from functional magnetic resonance (fMRI) images has been a major area of research for more than two decades.

Dictionary Learning

Higher-Order Block Term Decomposition for Spatially Folded fMRI Data

no code implementations15 Jul 2016 Christos Chatzichristos, Eleftherios Kofidis, Giannis Kopsinis, Sergios Theodoridis

The growing use of neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality.

Efficient KLMS and KRLS Algorithms: A Random Fourier Feature Perspective

no code implementations12 Jun 2016 Pantelis Bouboulis, Spyridon Pougkakiotis, Sergios Theodoridis

We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces (RKHS).

Robust Non-linear Regression: A Greedy Approach Employing Kernels with Application to Image Denoising

no code implementations4 Jan 2016 George Papageorgiou, Pantelis Bouboulis, Sergios Theodoridis

Finally, the proposed robust estimation framework is applied to the task of image denoising, and its enhanced performance in the presence of outliers is demonstrated.

Image Denoising regression

Complex Support Vector Machines for Regression and Quaternary Classification

no code implementations9 Mar 2013 Pantelis Bouboulis, Sergios Theodoridis, Charalampos Mavroforakis, Leoni Dalla

The method exploits the notion of widely linear estimation to model the input-out relation for complex-valued data and considers two cases: a) the complex data are split into their real and imaginary parts and a typical real kernel is employed to map the complex data to a complexified feature space and b) a pure complex kernel is used to directly map the data to the induced complex feature space.

Classification General Classification +1

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