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03/17/2023
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
Self-supervised learning is a central component in recent approaches to ...
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03/16/2023
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Distance-based classification is frequently used in transductive few-sho...
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01/21/2023
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2...
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05/18/2022
The Kernelized Taylor Diagram
This paper presents the kernelized Taylor diagram, a graphical framework...
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12/19/2021
RELAX: Representation Learning Explainability
Despite the significant improvements that representation learning via se...
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03/13/2021
Reconsidering Representation Alignment for Multi-view Clustering
Aligning distributions of view representations is a core component of to...
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09/25/2019
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels
Analyzing deep neural networks (DNNs) via information plane (IP) theory ...
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02/13/2019
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning rep...
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07/19/2018
The Deep Kernelized Autoencoder
Autoencoders learn data representations (codes) in such a way that the i...
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03/21/2018
Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a...
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11/17/2017
Bidirectional deep-readout echo state networks
We propose a deep architecture for the classification of multivariate ti...
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11/17/2017
Bidirectional deep-reservoir echo state networks
We propose a deep architecture for the classification of multivariate ti...
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11/17/2017
Bidirectional deep echo state networks
In this work we propose a deep architecture for the classification of mu...
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02/23/2017
Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
In this paper, we propose PCKID, a novel, robust, kernel function for sp...
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02/08/2017
Deep Kernelized Autoencoders
In this paper we introduce the deep kernelized autoencoder, a neural net...
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08/16/2016