Research map / Learning theory and algorithms / Matrix and tensor methods
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Modeling Sparse Deviations for Compressed Sensing using Generative Models (ICML 2018 · similarity 0.91)
- Compressed Sensing using Generative Models (ICML 2017 · similarity 0.91)
- Sparse Bayesian Generative Modeling for Compressive Sensing (NeurIPS 2024 · similarity 0.88)
- Recovery Guarantees for Posterior Sampling of One-Bit Compressed Sensing (NeurIPS 2026 · similarity 0.87)
- The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements (NeurIPS 2025 · similarity 0.87)
- QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models (AAAI 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.