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Found 24 papers, 9 papers with code
Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks
Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms.
Fairness-aware Optimal Graph Filter Design
We show that the optimal design of said filters can be cast as a convex problem in the graph spectral domain.
CoLiDE: Concomitant Linear DAG Estimation
We deal with the combinatorial problem of learning directed acyclic graph (DAG) structure from observational data adhering to a linear structural equation model (SEM).
Gradient-Based Spectral Embeddings of Random Dot Product Graphs
RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions.
Explainable Brain Age Prediction using coVariance Neural Networks
In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual.
Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features
To gauge the advantages offered by VNNs in neuroimaging data analysis, we focus on the task of "brain age" prediction using cortical thickness features.
Fairness-Aware Graph Filter Design
Graphs are mathematical tools that can be used to represent complex real-world systems, such as financial markets and social networks.
Dual-based Online Learning of Dynamic Network Topologies
We investigate online network topology identification from smooth nodal observations acquired in a streaming fashion.
pyGSL: A Graph Structure Learning Toolkit
Implementations of differentiable graph structure learning models are written in PyTorch, allowing us to leverage the rich software ecosystem that exists e. g., around logging, hyperparameter search, and GPU-communication.
Predicting Brain Age using Transferable coVariance Neural Networks
We have recently studied covariance neural networks (VNNs) that operate on sample covariance matrices using the architecture derived from graph convolutional networks, and we showed VNNs enjoy significant advantages over traditional data analysis approaches.
coVariance Neural Networks
Moreover, our experiments on multi-resolution datasets also demonstrate that VNNs are amenable to transferability of performance over covariance matrices of different dimensions; a feature that is infeasible for PCA-based approaches.
Learning Graph Structure from Convolutional Mixtures
Machine learning frameworks such as graph neural networks typically rely on a given, fixed graph to exploit relational inductive biases and thus effectively learn from network data.
Online Change Point Detection for Weighted and Directed Random Dot Product Graphs
Given a sequence of random (directed and weighted) graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution.
Learning to Model the Relationship Between Brain Structural and Functional Connectomes
Recent advances in neuroimaging along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level.
Accelerated Graph Learning from Smooth Signals
We consider network topology identification subject to a signal smoothness prior on the nodal observations.
Online Graph Learning under Smoothness Priors
The growing success of graph signal processing (GSP) approaches relies heavily on prior identification of a graph over which network data admit certain regularity.
Online Discriminative Graph Learning from Multi-Class Smooth Signals
Graph signal processing (GSP) is a key tool for satisfying the growing demand for information processing over networks.
Signal Processing on Directed Graphs
This paper provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs).
Graph topology inference benchmarks for machine learning
Graphs are nowadays ubiquitous in the fields of signal processing and machine learning.
Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information
This motivates formulating the topology inference task as an inverse problem, whereby one searches for a sparse GSO that is structurally admissible and approximately commutes with the observations' empirical covariance matrix.
Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals
Then, we consider the Bayesian scenario where we formulate the sampling task as the problem of maximizing a monotone weak submodular function, and propose a randomized-greedy algorithm to find a sub-optimal subset of informative nodes.
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation
We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the signal reconstruction mean squared error (MSE).
Decentralized learning for wireless communications and networking
This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data.
Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors
In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from \emph{incomplete streaming} data.
