Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging | Paper Citation Record

Observation d85ae6d9-43f3-4dc9-a78f-723a964d3f50 · outbound

This paper cites fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Reference 1

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This paper cites 2019 Deep learning in ultrasound imaging.Proceedings of the IEEE 108, 11–29.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2019 Deep learning in ultrasound imaging.Proceedings of the IEEE 108, 11–29

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.855208Z

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This paper cites 2022 Reframing Fast-Chirp FMCW Transceivers for Future Automotive Radar: The pathway to higher resolution.IEEE Solid-State Circuits Magazine 14, 44–55.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2022 Reframing Fast-Chirp FMCW Transceivers for Future Automotive Radar: The pathway to higher resolution.IEEE Solid-State Circuits Magazine 14, 44–55

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.844747Z

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This paper cites 2022 Score-based diffusion models for accelerated MRI.Medical image analysis 80, 102479.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2022 Score-based diffusion models for accelerated MRI.Medical image analysis 80, 102479

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.833189Z

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Observation 72dc20c1-df2f-4a72-a98d-d5ace54cc7ed · outbound

This paper cites 2022 High-resolution image synthesis with latent diffusion models.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2022 High-resolution image synthesis with latent diffusion models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.822680Z

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Observation 7e99b173-e5ef-42a7-b083-4e8cd467e7a1 · outbound

This paper cites The Llama 3 Herd of Models.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging The Llama 3 Herd of Models

Reference 6

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This paper cites 2006 Near-optimal signal recovery from random projections: Universal encoding strategies?.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2006 Near-optimal signal recovery from random projections: Universal encoding strategies?

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.811237Z

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Observation c6ca1197-b310-433a-ab75-d33afcd125e8 · outbound

This paper cites 2012 Compressed sensing: theory and applications.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2012 Compressed sensing: theory and applications

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.800285Z

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Observation eaaaec5a-1bc0-45bc-856c-6d03aafc420f · outbound

This paper cites 2015Sampling theory: Beyond bandlimited systems.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2015Sampling theory: Beyond bandlimited systems

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.788866Z

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Observation 6d00b59d-c7ef-489e-843e-5af901bcf9c2 · outbound

This paper cites 2018 A systematic review of compressive sensing: Concepts, implementations and applications.IEEE access 6, 4875–4894.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2018 A systematic review of compressive sensing: Concepts, implementations and applications.IEEE access 6, 4875–4894

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.776921Z

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Observation 9576dd9e-d619-4d67-ab71-a2740e3f12cc · outbound

This paper cites 2014 Fourier-domain beamforming: the path to compressed ultrasound imaging.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2014 Fourier-domain beamforming: the path to compressed ultrasound imaging

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.764914Z

Source-reported events for the cited work

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Observation e014ac7b-33d0-4625-a2a1-162895326448 · outbound

This paper cites 2019 Strategic undersampling and recovery using compressed sensing for enhancing ultrasound image quality.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2019 Strategic undersampling and recovery using compressed sensing for enhancing ultrasound image quality

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.754409Z

Source-reported events for the cited work

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Observation 8388f5f3-a61a-4c7f-a499-2ca609d48add · outbound

This paper cites 2020 Learning sub-sampling and signal recovery with applications in ultrasound imaging.IEEE Transactions on Medical Imaging 39, 3955–3966.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2020 Learning sub-sampling and signal recovery with applications in ultrasound imaging.IEEE Transactions on Medical Imaging 39, 3955–3966

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.743466Z

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This paper cites 2011 Robust recovery of synthetic aperture radar data from uniformly under-sampled measurements.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2011 Robust recovery of synthetic aperture radar data from uniformly under-sampled measurements

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.731207Z

Source-reported events for the cited work

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Observation 1326f591-0bfc-413e-aca9-dcf6260b2e91 · outbound

This paper cites 2018 Sub-Nyquist radar systems: Temporal, spectral, and spatial compression.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2018 Sub-Nyquist radar systems: Temporal, spectral, and spatial compression

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.719629Z

Source-reported events for the cited work

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Observation 327e064a-e667-4b61-a9c0-f86588012e77 · outbound

This paper cites 2019 Compressed sensing MRI: a review from signal processing perspective.BMC Biomedical Engineering 1, 8.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2019 Compressed sensing MRI: a review from signal processing perspective.BMC Biomedical Engineering 1, 8

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.707859Z

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Observation a8a5f20b-cd94-49f7-8522-43a0d3e7b29b · outbound

This paper cites 2023 Model-Based Deep Learning.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2023 Model-Based Deep Learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.696393Z

Source-reported events for the cited work

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This paper cites an unresolved cited work.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:40:24.685506Z

Source-reported events for the cited work

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Observation 92832769-0af9-4365-a40f-44dd1530b8d8 · outbound

This paper cites Auto-Encoding Variational Bayes.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Auto-Encoding Variational Bayes

Reference 19

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Observation 3e16c520-0769-4cd1-a4d1-fb5b15522afc · outbound

This paper cites 2014 Generative adversarial nets.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2014 Generative adversarial nets

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.673968Z

Source-reported events for the cited work

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Observation 2b4658bf-962a-4def-ba27-e7f9e38700d3 · outbound

This paper cites 2018 Glow: Generative flow with invertible 1x1 convolutions.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2018 Glow: Generative flow with invertible 1x1 convolutions

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.663023Z

Source-reported events for the cited work

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Observation 26c33fa8-9897-482e-976d-973157665ed8 · outbound

This paper cites 2020 Score-Based Generative Modeling through Stochastic Differential Equations.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2020 Score-Based Generative Modeling through Stochastic Differential Equations

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.651631Z

Source-reported events for the cited work

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This paper cites Advances in neural information processing systems 32.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Advances in neural information processing systems 32

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.641353Z

Source-reported events for the cited work

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Observation 6bdfc969-3552-43cc-b1d0-c21ea221d041 · outbound

This paper cites 2024 A regularized conditional GAN for posterior sampling in image recovery problems.Advances in Neural Information Processing Systems 36.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2024 A regularized conditional GAN for posterior sampling in image recovery problems.Advances in Neural Information Processing Systems 36

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.629725Z

Source-reported events for the cited work

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This paper cites 2023 Pseudoinverse-guided diffusion models for inverse problems.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2023 Pseudoinverse-guided diffusion models for inverse problems

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.616919Z

Source-reported events for the cited work

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Observation e2d98e7f-188d-4abb-a2cc-aa68c316722b · outbound

This paper cites A Variational Perspective on Solving Inverse Problems with Diffusion Models.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging A Variational Perspective on Solving Inverse Problems with Diffusion Models

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Source-reported events for the cited work

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Observation 482c059d-79f5-453f-8d45-9f50a11547c5 · outbound

This paper cites A Survey on Diffusion Models for Inverse Problems.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging A Survey on Diffusion Models for Inverse Problems

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Observation b7b59bac-4542-4857-b5b8-8e1fa1aebd52 · outbound

This paper cites Diffusion Posterior Sampling for General Noisy Inverse Problems.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Diffusion Posterior Sampling for General Noisy Inverse Problems

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Observation 2c433626-6bd2-4a3b-be23-881b1369fd5c · outbound

This paper cites 2011 Tweedie’s formula and selection bias.Journal of the American Statistical Association 106, 1602–1614.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2011 Tweedie’s formula and selection bias.Journal of the American Statistical Association 106, 1602–1614

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.604916Z

Source-reported events for the cited work

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Observation eb94737c-0e7c-4a96-b66f-c74328b96323 · outbound

This paper cites Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eac2150b-1cb1-4d0f-a175-77d03f8b46fa · outbound

This paper cites 2024 Model-Based Diffusion for Mitigating Automotive Radar Interference.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2024 Model-Based Diffusion for Mitigating Automotive Radar Interference

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.592257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f475e07e-f9b9-4fca-82b2-9da9c2e64a4f · outbound

This paper cites 2024 Dehazing Ultrasound using Diffusion Models.IEEE Transactions on Medical Imaging pp.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2024 Dehazing Ultrasound using Diffusion Models.IEEE Transactions on Medical Imaging pp

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.580043Z

Source-reported events for the cited work

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Observation 3640f253-2ab0-4115-a14e-e3484d50b93f · outbound

This paper cites an unresolved cited work.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-16T12:40:24.566305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2884f298-d522-44f7-b1e0-1d9972badf86 · outbound

This paper cites 2021 Compressed ultrasound imaging: From sub-Nyquist rates to super resolution.IEEE BITS the information theory magazine 1, 27–44.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2021 Compressed ultrasound imaging: From sub-Nyquist rates to super resolution.IEEE BITS the information theory magazine 1, 27–44

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Source-reported events for the cited work

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Observation 2596b13c-bd47-45f8-a0eb-9e349caaebaf · outbound

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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2019 The generalized contrast-to-noise ratio: A formal definition for lesion detectability

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 02be8d36-bba6-431c-91f6-db5f90e6d5dc · outbound

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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2025 Unlimited sampling beyond modulo

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4d853c52-4178-4ec7-86c7-45d43f40779b · outbound

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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2021 Automotive Radar — From First Efforts to Future Systems

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.517350Z

Source-reported events for the cited work

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Observation c94f22a8-49c6-4314-8ff8-b44212ca3526 · outbound

This paper cites 2016 Chirp diversity waveform design and detection by stretch processing.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2016 Chirp diversity waveform design and detection by stretch processing

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d06b238e-8734-4542-8f9a-725161bbe7f1 · outbound

This paper cites 1991 A stepped chirp technique for range resolution enhancement.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 1991 A stepped chirp technique for range resolution enhancement

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 421ea064-197e-462a-8dff-3c5e84ce0ed4 · outbound

This paper cites 2017 Automotive radar interference mitigation using a sparse sampling approach.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2017 Automotive radar interference mitigation using a sparse sampling approach

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a42d48b8-f5e1-4019-ac6e-67ff204b2115 · outbound

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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2020 Automotive Radar Signal Interference Mitigation Using RNN with Self Attention

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.468918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a039baa5-8578-4482-8be7-6088d920165d · outbound

This paper cites 2020 Automotive Radar Interference Mitigation using a Convolutional Autoencoder.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2020 Automotive Radar Interference Mitigation using a Convolutional Autoencoder

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cc2d13bf-be67-4875-aafa-16303562d5a1 · outbound

This paper cites 2021 Estimating the Magnitude and Phase of Automotive Radar Signals Under Multiple Interference Sources With Fully Convolutional Networks.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2021 Estimating the Magnitude and Phase of Automotive Radar Signals Under Multiple Interference Sources With Fully Convolutional Networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.443388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation da6c9d15-56bc-4105-91db-c9d53f5f649f · outbound

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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2021 Complex-valued convolutional neural networks for enhanced radar signal denoising and interference mitigation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.430253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 041a4044-92c0-44d2-bc99-5dd2480a6375 · outbound

This paper cites 2023 Signal Reconstruction for FMCW Radar Interference Mitigation Using Deep Unfolding.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2023 Signal Reconstruction for FMCW Radar Interference Mitigation Using Deep Unfolding

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.417765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8f6aae4c-80bc-4e6a-9856-34006bd9557d · outbound

This paper cites 2024 Neurally Augmented Deep Unfolding for Automotive Radar Interference Mitigation.IEEE Transactions on Radar Systems 2, 712–724.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2024 Neurally Augmented Deep Unfolding for Automotive Radar Interference Mitigation.IEEE Transactions on Radar Systems 2, 712–724

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.405856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6876d2e9-714b-4ef2-9dd5-a856cd606735 · outbound

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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2025 Sequential Posterior Sampling with Diffusion Models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.392874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a10020b4-84cd-44ac-8e86-be6b91e8f1a8 · outbound

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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2010 Learning fast approximations of sparse coding

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.381061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1ee355bf-4f6a-4ce8-896b-eb5e99aabc90 · outbound

This paper cites 2023 Learning to sample: Data-driven sampling and reconstruction of FRI signals.IEEE Access 11, 71048–71062.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2023 Learning to sample: Data-driven sampling and reconstruction of FRI signals.IEEE Access 11, 71048–71062

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.370230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7f8a0c49-8937-490b-9cf9-0aed64153494 · outbound

This paper cites an unresolved cited work.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:40:24.358424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3c9d561e-1674-4cd8-b497-5acccacd044a · outbound

This paper cites 2022 Deep Unfolding With Normalizing Flow Priors for Inverse Problems.IEEE Transactions on Signal Processing 70, 2962–2971.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2022 Deep Unfolding With Normalizing Flow Priors for Inverse Problems.IEEE Transactions on Signal Processing 70, 2962–2971

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.346321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e8294872-3c2d-451f-b0bb-3116c91ba3ba · outbound

This paper cites Unrolled Generative Adversarial Networks.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Unrolled Generative Adversarial Networks

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:23.938918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04385a2d-e6e0-4966-a521-2040ccb56242 · outbound

This paper cites Compressing GANs using Knowledge Distillation.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Compressing GANs using Knowledge Distillation

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-16T12:40:24.091039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f6b0cc20-f59a-40ad-9e0a-425fbe059d94 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Progressive Distillation for Fast Sampling of Diffusion Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:23.947225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b9f722f-09c9-4748-804c-b25361951ecf · outbound

This paper cites Solving Inverse Problems in Medical Imaging with Score-Based Generative Models.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:23.951395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19f524be-02de-425d-89ac-370fcf3db6bd · outbound

This paper cites 2019 Inverse GANs for accelerated MRI reconstruction.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2019 Inverse GANs for accelerated MRI reconstruction

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.333813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bb6504a2-0393-4e89-aa2c-47676ac2dbe1 · outbound

This paper cites 2015 Info-greedy sequential adaptive compressed sensing.IEEE Journal of selected topics in signal processing 9, 601–611.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2015 Info-greedy sequential adaptive compressed sensing.IEEE Journal of selected topics in signal processing 9, 601–611

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.321342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a3484dd5-7790-4521-8998-65099200f4a2 · outbound

This paper cites 2021 Active deep probabilistic subsampling.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2021 Active deep probabilistic subsampling

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.309223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8a1601b6-d61e-4483-a2a9-bfaf1398b36d · outbound

This paper cites 2020 Experimental design for MRI by greedy policy search.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2020 Experimental design for MRI by greedy policy search

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.296488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f7de0d2d-b63b-4b3f-96f7-0ba57346764d · outbound

This paper cites 2022 Accelerated intravascular ultrasound imaging using deep reinforcement learning.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2022 Accelerated intravascular ultrasound imaging using deep reinforcement learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.283148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8a55ada9-49ba-4610-a89f-4e36107822a2 · outbound

This paper cites 2020 Uncertainty-driven adaptive sampling via GANs.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2020 Uncertainty-driven adaptive sampling via GANs

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.269615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a7b9fd04-aefc-4024-be17-4f5ae10b80c5 · outbound

This paper cites 2023 Active Subsampling Using Deep Generative Models by Maximizing Expected Information Gain.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2023 Active Subsampling Using Deep Generative Models by Maximizing Expected Information Gain

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.257175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 96801c5d-c0d2-418a-a1ec-9e015f23cdf0 · outbound

This paper cites 2017 Improved training of wasserstein gans.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2017 Improved training of wasserstein gans

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.243770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a703ab6b-ee9e-4c0b-b5f5-df6deefd5d7d · outbound

This paper cites 2007 Approximating the Kullback Leibler divergence between Gaussian mixture models.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2007 Approximating the Kullback Leibler divergence between Gaussian mixture models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.231093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8c412a1c-2bfd-4dc7-992a-9425b0fcb195 · outbound

This paper cites 1998 Gradient-based learning applied to document recognition.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 1998 Gradient-based learning applied to document recognition

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.217766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b664d8d6-2d3f-42c9-b7fc-05d27eb46901 · outbound

This paper cites Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-16T12:40:24.049728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a1dd5648-8c55-47a2-9eea-465f7e80e2cd · outbound

This paper cites 2022 Denoising diffusion restoration models.Advances in Neural Information Processing Systems 35, 23593–23606.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2022 Denoising diffusion restoration models.Advances in Neural Information Processing Systems 35, 23593–23606

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.205586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0264a1fc-e923-4279-bc62-b48340a1790a · outbound

This paper cites 2025 Active Diffusion Subsampling.

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging 2025 Active Diffusion Subsampling

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.193616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers