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Found 11 papers, 7 papers with code
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
We introduce a novel loss function for training deep learning architectures to perform classification.
A Unified Deep Learning Formalism For Processing Graph Signals
Convolutional Neural Networks are very efficient at processing signals defined on a discrete Euclidean space (such as images).
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction
Predicting the future of Graph-supported Time Series (GTS) is a key challenge in many domains, such as climate monitoring, finance or neuroimaging.
Deep geometric knowledge distillation with graphs
Specifically we introduce a graph-based RKD method, in which graphs are used to capture the geometry of latent spaces.
Predicting the Accuracy of a Few-Shot Classifier
In the context of few-shot learning, one cannot measure the generalization ability of a trained classifier using validation sets, due to the small number of labeled samples.
Few-shot Decoding of Brain Activation Maps
Few-shot learning addresses problems for which a limited number of training examples are available.
Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs
Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task.
Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification
It is very common to face classification problems where the number of available labeled samples is small compared to their dimension.
Graphs as Tools to Improve Deep Learning Methods
This chapter is composed of four main parts: tools for visualizing intermediate layers in a DNN, denoising data representations, optimizing graph objective functions and regularizing the learning process.
Studying Limits of Explainability by Integrated Gradients for Gene Expression Models
The scientific questions are formulated as classical learning problems on tabular data or on graphs, e. g. phenotype prediction from gene expression data.
A Comparative Analysis of Gene Expression Profiling by Statistical and Machine Learning Approaches
A collection of machine learning models including logistic regression, multilayer perceptron, and graph neural network are trained to classify samples according to their cancer type.
