1 citations · 2 across the 17 of their papers we have counts for
18 papers
Strange-quark-number-dependent response to partonic dynamics in baryon-rich Au+Au collisions
Di Zhao, Xun Zhu, Gao-Chan Yong
We identify a strange-quark-number-dependent hierarchy in the response to explicit partonic dynamics by comparing PHSD5.2 with its hadron--string limit in baryon-rich Au+Au collisi…
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…
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…
Scalable Implicit Graphon Learning
Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
Graphons are continuous models that represent the structure of graphs and allow the generation of graphs of varying sizes. We propose Scalable Implicit Graphon Learning (SIGL), a s…
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)…
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…
Solving Linear Inverse Problems using Higher-Order Annealed Langevin Diffusion
Nicolas Zilberstein, Ashutosh Sabharwal, Santiago Segarra
We propose a solution for linear inverse problems based on higher-order Langevin diffusion. More precisely, we propose pre-conditioned second-order and third-order Langevin dynamic…
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…
Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Our detector achieves state-of-the…
Annealed Langevin Dynamics for Massive MIMO Detection
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is…
Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Hence, the objective of any detector of practical relevance is to get r…
Robust MIMO Detection using Hypernetworks with Learned Regularizers
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Recently, there has been a growing interest to get reasonably close to…
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…