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José M. F. Moura

4 papers hereh-index 210 citations4 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • eess.IV1
same name
  • José M. F. Moura — 48 papers, h 75
  • José M. F. Moura — 10 papers, h 4
  • José M. F. Moura — 2 papers
  • José M. F. Moura — 2 papers, h 5
  • José M. F. Moura — 2 papers, h 3
  • José M. F. Moura — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
collaborators

4 papers

cs.LG2026

The Powers of Precision: Structure-Informed Detection in Complex Systems -- From Customer Churn to Seizure Onset

Augusto Santos, Teresa Santos, Catarina Rodrigues +1

Emergent phenomena -- onset of epileptic seizures, sudden customer churn, or pandemic outbreaks -- often arise from hidden causal interactions in complex systems. We propose a mach…

eess.IV2025

Inferring the Graph Structure of Images for Graph Neural Networks

Mayur S Gowda, John Shi, Augusto Santos +1

Image datasets such as MNIST are a key benchmark for testing Graph Neural Network (GNN) architectures. The images are traditionally represented as a grid graph with each node repre…

cs.LG2023

Inferring the Graph of Networked Dynamical Systems under Partial Observability and Spatially Colored Noise

Augusto Santos, Diogo Rente, Rui Seabra +1

In a Networked Dynamical System (NDS), each node is a system whose dynamics are coupled with the dynamics of neighboring nodes. The global dynamics naturally builds on this network…

cs.LG2023

Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise

Augusto Santos, Diogo Rente, Rui Seabra +1

This paper considers learning the hidden causal network of a linear networked dynamical system (NDS) from the time series data at some of its nodes -- partial observability. The dy…

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