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Equivariant Priors for Compressed Sensing with Arash Behboodi - #584

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Manage episode 335416038 series 2355587
Player FM과 저희 커뮤니티의 TWIML and Sam Charrington 콘텐츠는 모두 원 저작자에게 속하며 Player FM이 아닌 작가가 저작권을 갖습니다. 오디오는 해당 서버에서 직접 스트리밍 됩니다. 구독 버튼을 눌러 Player FM에서 업데이트 현황을 확인하세요. 혹은 다른 팟캐스트 앱에서 URL을 불러오세요.

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

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651 에피소드

공유
 
Manage episode 335416038 series 2355587
Player FM과 저희 커뮤니티의 TWIML and Sam Charrington 콘텐츠는 모두 원 저작자에게 속하며 Player FM이 아닌 작가가 저작권을 갖습니다. 오디오는 해당 서버에서 직접 스트리밍 됩니다. 구독 버튼을 눌러 Player FM에서 업데이트 현황을 확인하세요. 혹은 다른 팟캐스트 앱에서 URL을 불러오세요.

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

  continue reading

651 에피소드

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