35 min listen
Equivariant Priors for Compressed Sensing with Arash Behboodi - #584
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Equivariant Priors for Compressed Sensing with Arash Behboodi - #584
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
ratings:
Length:
40 minutes
Released:
Jul 25, 2022
Format:
Podcast episode
Description
Today we’re joined by Arash Behboodi, a machine learning researcher at Qualcomm Technologies. In our conversation with Arash, we explore his paper Equivariant Priors for Compressed Sensing with Unknown Orientation, which proposes using equivariant generative models as a prior means to show that signals with unknown orientations can be recovered with iterative gradient descent on the latent space of these models and provide additional theoretical recovery guarantees. We discuss the differences between compression and compressed sensing, how he was able to evolve a traditional VAE architecture to understand equivalence, and some of the research areas he’s applying this work, including cryo-electron microscopy. We also discuss a few of the other papers that his colleagues have submitted to the conference, including Overcoming Oscillations in Quantization-Aware Training, Variational On-the-Fly Personalization, and CITRIS: Causal Identifiability from Temporal Intervened Sequences.
The complete show notes for this episode can be found at twimlai.com/go/584
The complete show notes for this episode can be found at twimlai.com/go/584
Released:
Jul 25, 2022
Format:
Podcast episode
Titles in the series (100)
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