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Found 13 papers, 6 papers with code
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.
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.
Clustering with Simplicial Complexes
Then, leveraging the Cheeger inequality, we propose the simplicial spectral clustering algorithm.
A Simple Way to Learn Metrics Between Attributed Graphs
Therefore, many metric learning algorithms have been developed in recent years, mainly for Euclidean data in order to improve performance of classification or clustering methods.
Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data
The main scientific message is that most of the time, training neural networks for predicting extreme heatwaves occurs in a regime of lack of data.
Fast Multiscale Diffusion on Graphs
Diffusing a graph signal at multiple scales requires computing the action of the exponential of several multiples of the Laplacian matrix.
Deep Learning-based Extreme Heatwave Forecast
The present work explores the use of deep learning architectures, trained using outputs of a climate model, as an alternative strategy to forecast the occurrence of extreme long-lasting heatwaves.
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis
We present a novel multiview canonical correlation analysis model based on a variational approach.
Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
We propose a novel algorithm for unsupervised graph representation learning with attributed graphs.
Solving NMF with smoothness and sparsity constraints using PALM
Non-negative matrix factorization is a problem of dimensionality reduction and source separation of data that has been widely used in many fields since it was studied in depth in 1999 by Lee and Seung, including in compression of data, document clustering, processing of audio spectrograms and astronomy.
Harmonic analysis on directed graphs and applications: from Fourier analysis to wavelets
We introduce a novel harmonic analysis for functions defined on the vertices of a strongly connected directed graph of which the random walk operator is the cornerstone.
Design of graph filters and filterbanks
The aim of this chapter is to review general concepts for the introduction of filters and representations of graph signals.
Signal Processing Information Theory Social and Information Networks Information Theory
Accelerated Spectral Clustering Using Graph Filtering Of Random Signals
We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm.
Social and Information Networks Numerical Analysis
