1 citations · 1 across the 4 of their papers we have counts for
4 papers
Model-Driven Graph Contrastive Learning
Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
We propose , a model-driven graph contrastive learning (GCL) framework that leverages graphons (probabilistic generative models for graphs) to guide contrastive lear…
Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation
Victor M. Tenorio, Nicolas Zilberstein, Santiago Segarra +1
Diffusion models have emerged as powerful generative models for graph generation, yet their use for conditional graph generation remains a fundamental challenge. In particular, gui…
Joint channel estimation and data detection in massive MIMO systems based on diffusion models
Nicolas Zilberstein, Ananthram Swami, Santiago Segarra
We propose a joint channel estimation and data detection algorithm for massive multilple-input multiple-output systems based on diffusion models. Our proposed method solves the bli…
Unsupervised Learning of Sampling Distributions for Particle Filters
Fernando Gama, Nicolas Zilberstein, Martin Sevilla +2
Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating tr…