1 citations · 2 across the 17 of their papers we have counts for
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Feasible Flow Matching for Graph Reconstruction via Within-Sampling Primal-Dual Guidance
Haoming Chen, Nicolas Zilberstein, Santiago Paternain +1
Graph reconstruction from partial observations often comes with structural side information, such as degree bounds, triangle counts, or an edge-density band. Prior-Informed Flow Ma…
Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems
Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithf…
Learning Normalized Energy Models for Linear Inverse Problems
Nicolas Zilberstein, Santiago Segarra, Eero Simoncelli +1
Generative diffusion models can provide powerful prior probability models for inverse problems in imaging, but existing implementations suffer from two key limitations: the p…
Prior-Informed Flow Matching for Graph Reconstruction
Harvey Chen, Nicolas Zilberstein, Santiago Segarra
We introduce \textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction. Reconstructing graphs from partial observations remains a key challeng…
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…
Repulsive Latent Score Distillation for Solving Inverse Problems
Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
Score Distillation Sampling (SDS) has been pivotal for leveraging pre-trained diffusion models in downstream tasks such as inverse problems, but it faces two major challenges: $(i)…
Unrolling Particles: Unsupervised Learning of Sampling Distributions
Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk +1
Particle filtering is used to compute good nonlinear estimates of complex systems. It samples trajectories from a chosen distribution and computes the estimate as a weighted averag…