#compressed-sensing News & Analysis

3 articles tagged with #compressed-sensing. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · Jun 56/10

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Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime

Researchers present a theoretical framework analyzing scaling laws for shallow neural networks in the feature learning regime, deriving phase diagrams that connect sample complexity and weight decay to risk exponents. The work bridges empirical observations in deep learning with rigorous mathematical analysis, establishing links between weight spectrum properties and generalization performance through matrix compressed sensing and LASSO theory.

AINeutralarXiv – CS AI · Jun 26/10

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Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications

Researchers demonstrate a flow-based generative model that optimizes sampling strategies for compressed sensing, achieving state-of-the-art reconstruction results using only 5% of measurements. The framework combines task-aware learning with flow matching to enhance performance across image classification, reconstruction, and MRI acceleration applications.

AINeutralarXiv – CS AI · Mar 116/10

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Latent Generative Models with Tunable Complexity for Compressed Sensing and other Inverse Problems

Researchers developed tunable-complexity priors for generative models (diffusion models, normalizing flows, and variational autoencoders) that can dynamically adjust complexity based on the specific inverse problem. The approach uses nested dropout and demonstrates superior performance across compressed sensing, inpainting, denoising, and phase retrieval tasks compared to fixed-complexity baselines.