Nicolas Zilberstein

According to our database, Nicolas Zilberstein authored at least 18 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Learning Normalized Energy Models for Linear Inverse Problems.

Nicolas Zilberstein, Santiago Segarra, Eero P. Simoncelli, Florentin Guth

CoRR, May, 2026

Prior-Informed Flow Matching for Graph Reconstruction.

Harvey Chen, Nicolas Zilberstein, Santiago Segarra

CoRR, January, 2026

2025

Model-Driven Graph Contrastive Learning.

Ali Azizpour, Nicolas Zilberstein, Santiago Segarra

CoRR, June, 2025

Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation.

Victor M. Tenorio, Nicolas Zilberstein, Santiago Segarra, Antonio G. Marques

CoRR, May, 2025

Repulsive Latent Score Distillation for Solving Inverse Problems.

Nicolas Zilberstein, Morteza Mardani, Santiago Segarra

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Scalable Implicit Graphon Learning.

Ali Azizpour, Nicolas Zilberstein, Santiago Segarra

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials.

Nicolas Zilberstein, Santiago Segarra, Luiz Chamon

Proceedings of the 59th Asilomar Conference on Signals, 2025

2024

Solving Linear Inverse Problems Using Higher-Order Annealed Langevin Diffusion.

Nicolas Zilberstein, Ashutosh Sabharwal, Santiago Segarra

IEEE Trans. Signal Process., 2024

Repulsive Score Distillation for Diverse Sampling of Diffusion Models.

Nicolas Zilberstein, Morteza Mardani, Santiago Segarra

CoRR, 2024

Joint Channel Estimation and Data Detection in Massive Mimo Systems Based on Diffusion Models.

Nicolas Zilberstein, Ananthram Swami, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2024

End-to-End Learning of Gaussian Mixture Proposals Using Differentiable Particle Filters and Neural Networks.

Benjamin Cox, Sara Pérez-Vieites, Nicolas Zilberstein, Martin Sevilla, Santiago Segarra, Víctor Elvira

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Annealed Langevin Dynamics for Massive MIMO Detection.

Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra

IEEE Trans. Wirel. Commun., June, 2023

Unsupervised Learning of Sampling Distributions for Particle Filters.

Fernando Gama, Nicolas Zilberstein, Martin Sevilla, Richard G. Baraniuk, Santiago Segarra

IEEE Trans. Signal Process., 2023

Accelerated Massive MIMO Detector Based on Annealed Underdamped Langevin Dynamics.

Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2023

State and Dynamics Estimation with the Kalman-Langevin filter.

Martin Sevilla, Nicolas Zilberstein, Benjamin Cox, Sara Pérez-Vieites, Víctor Elvira, Santiago Segarra

Proceedings of the 57th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2023, Pacific Grove, CA, USA, October 29, 2023

2022

Unrolling Particles: Unsupervised Learning of Sampling Distributions.

Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk, Santiago Segarra

Proceedings of the IEEE International Conference on Acoustics, 2022

Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics.

Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra

Proceedings of the 30th European Signal Processing Conference, 2022

Robust MIMO Detection using Hypernetworks with Learned Regularizers.

Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra

Proceedings of the 30th European Signal Processing Conference, 2022