mlsanityData to 14 Oct 2026

Adaptive sampling using variational autoencoder and reinforcement learning

arXiv 2512.03525 ↗First posted 3 Dec 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL with diffusion and flow models

Abstract (arXiv)

Compressed sensing enables sparse sampling but relies on generic bases and random measurements, limiting efficiency and reconstruction quality. Optimal sensor placement uses historcal data to design tailored sampling patterns, yet its fixed, linear bases cannot adapt to nonlinear or sample-specific variations. Generative model-based compressed sensing improves reconstruction using deep generative priors but still employs suboptimal random sampling. We propose an adaptive sparse sensing framework that couples a variational autoencoder prior with reinforcement learning to select measurements sequentially. Experiments show that this approach outperforms CS, OSP, and Generative model-based reconstruction from sparse measurements.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 9 references that are in the map. First posted on arXiv 3 Dec 2025.

Builds on

3 of 9 references.

References · 3

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Compressed Sensing using Generative Modelscited by 339
  3. 2017Data-Driven Sparse Sensor Placement for Reconstructioncited by 57

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  1. 2026Active Learning for Conditional Generative Compressed Sensing
  2. 2026Classification-oriented adaptive sensing via posterior sampling
  3. 2026Tunable Latent Generative Priors for Compressed Sensing and Inverse Problems
  4. 2026Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
  5. 2024Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
  6. 2025Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascade
  7. 2024Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling
  8. 2026Hierarchical Variational Policies for Reward-Guided Diffusion