Search Results for author:
Found 22 papers, 5 papers with code
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework to optimize the hyper-parameters.
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems.
Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners
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
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.
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
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.
Ranked #2 on
Adversarial Robustness
on CIFAR-10
Dialog speech sentiment classification for imbalanced datasets
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.
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning
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.
Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior
However, the optimal determination of a tensor rank is known to be a non-deterministic polynomial-time hard (NP-hard) task.
Early soft and flexible fusion of EEG and fMRI via tensor decompositions
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.
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
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.
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition
Hidden Markov Models (HMMs) comprise a powerful generative approach for modeling sequential data and time-series in general.
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
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.
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes
Then, we propose a novel SM kernel with a dependency structure (SMD) by using cross-convolution between the SM components.
Nonparametric Bayesian Deep Networks with Local Competition
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.
Unsupervised learning of the brain connectivity dynamic using residual D-net
In this paper, we propose a novel unsupervised learning method to learn the brain dynamics using a deep learning architecture named residual D-net.
Information Assisted Dictionary Learning for fMRI data analysis
The new method allows the incorporation of a priori knowledge associated both with the experimental design as well as with available brain Atlases.
Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features
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
Extracting information from functional magnetic resonance (fMRI) images has been a major area of research for more than two decades.
Higher-Order Block Term Decomposition for Spatially Folded fMRI Data
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
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
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.
Complex Support Vector Machines for Regression and Quaternary Classification
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.
