Equivariant Priors for Compressed Sensing with Arash Behboodi - #584

This article summarizes a podcast episode featuring Arash Behboodi, a machine learning researcher. The core discussion revolves around his paper on using equivariant generative models for compressed sensing, specifically addressing signals with unknown orientations. The research explores recovering these signals using iterative gradient descent on the latent space of these models, offering theoretical recovery guarantees. The conversation also touches upon the evolution of VAE architectures to understand equivalence and the application of this work in areas like cryo-electron microscopy. Furthermore, the episode mentions related research papers submitted by Behboodi's colleagues, broadening the scope of the discussion to include quantization-aware training, personalization, and causal identifiability.

• •Equivariant generative models are proposed as a prior for compressed sensing.
• •Signals with unknown orientations can be recovered using iterative gradient descent.
• •The research has applications in areas like cryo-electron microscopy.