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01/16/2023
Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach
The field of visual few-shot classification aims at transferring the sta...
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12/13/2022
A Statistical Model for Predicting Generalization in Few-Shot Classification
The estimation of the generalization error of classifiers often relies o...
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03/09/2022
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
Graph Signal Processing is a promising framework to manipulate brain sig...
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01/24/2022
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Few-shot learning aims at leveraging knowledge learned by one or more de...
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10/08/2021
Graphs as Tools to Improve Deep Learning Methods
In recent years, deep neural networks (DNNs) have known an important ris...
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01/12/2021
Improving Classification Accuracy with Graph Filtering
In machine learning, classifiers are typically susceptible to noise in t...
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06/30/2020
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies
In this paper, we introduce a novel and interpretable methodology to clu...
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10/27/2017
Convolutional neural networks on irregular domains through approximate translations on inferred graphs
We propose a generalization of convolutional neural networks (CNNs) to i...
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09/12/2017
Translations on graphs with neighborhood preservation
In the field of graph signal processing, defining translation operators ...
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03/06/2017
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
Graph Signal Processing (GSP) is a promising framework to analyze multi-...
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04/27/2016