1 citations · 2 across the 17 of their papers we have counts for
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Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials
Nicolas Zilberstein, Santiago Segarra, Luiz Chamon
We introduce shielded Langevin Monte Carlo (LMC), a constrained sampler inspired by navigation functions, capable of sampling from unnormalized target distributions defined over pu…
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…