In this discussion, Arash Behboodi, a machine learning researcher at Qualcomm Technologies, dives deep into his groundbreaking paper on equivariant generative models for compressed sensing. He explains how these models can recover signals with unknown orientations, offering theoretical recovery guarantees. The conversation touches on evolving VAE architectures, the challenges of signal recovery in wireless communication, and the exciting applications of his work in fields like cryo-electron microscopy. Additionally, they explore innovative strategies in quantization-aware training and temporal causal identifiability.
