
TLDR: Twin Learning for Dimensionality Reduction
Dimensionality reduction methods are unsupervised approaches which learn...
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Graphs as Tools to Improve Deep Learning Methods
In recent years, deep neural networks (DNNs) have known an important ris...
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SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
In neural Information Retrieval (IR), ongoing research is directed towar...
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Improving Classification Accuracy with Graph Filtering
In machine learning, classifiers are typically susceptible to noise in t...
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Graphs for deep learning representations
In recent years, Deep Learning methods have achieved state of the art pe...
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DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems
Learning deep representations to solve complex machine learning tasks ha...
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Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs
Measuring the generalization performance of a Deep Neural Network (DNN) ...
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Representing Deep Neural Networks Latent Space Geometries with Graphs
Deep Learning (DL) has attracted a lot of attention for its ability to r...
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Graph topology inference benchmarks for machine learning
Graphs are nowadays ubiquitous in the fields of signal processing and ma...
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Deep geometric knowledge distillation with graphs
In most cases deep learning architectures are trained disregarding the a...
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Improved Visual Localization via Graph Smoothing
Vision based localization is the problem of inferring the pose of the ca...
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Structural Robustness for Deep Learning Architectures
Deep Networks have been shown to provide stateoftheart performance in...
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Comparing linear structurebased and datadriven latent spatial representations for sequence prediction
Predicting the future of Graphsupported Time Series (GTS) is a key chal...
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Attention Based Pruning for Shift Networks
In many application domains such as computer vision, Convolutional Layer...
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A Unified Deep Learning Formalism For Processing Graph Signals
Convolutional Neural Networks are very efficient at processing signals d...
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Introducing Graph Smoothness Loss for Training Deep Learning Architectures
We introduce a novel loss function for training deep learning architectu...
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Carlos Lassance
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