CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction | Paper Citation Record

Observation 02f81aab-88f3-4e59-a914-841a5136146c · outbound

This paper cites Modern diagnostic imaging technique applications and risk factors in the medical field: a review,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Modern diagnostic imaging technique applications and risk factors in the medical field: a review,

Reference 1

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Observation d799dae7-b482-4c5f-ac70-4686454e96e4 · outbound

This paper cites Next-generation mri scanner designed for ultra- high-resolution human brain imaging at 7 tesla,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Next-generation mri scanner designed for ultra- high-resolution human brain imaging at 7 tesla,

Reference 2

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Observation 236d7b72-4426-4caa-b2ac-b89b2c1feb8e · outbound

This paper cites 2.5-minute fast brain mri with multiple contrasts in acute ischemic stroke,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction 2.5-minute fast brain mri with multiple contrasts in acute ischemic stroke,

Reference 3

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Observation 9fcf093b-f157-4e04-9b1b-d88f1b25b9fc · outbound

This paper cites Emerging techniques in cardiac magnetic resonance imaging,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Emerging techniques in cardiac magnetic resonance imaging,

Reference 4

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Observation 3ec60201-b4f0-4d0d-a289-11c4c4939cba · outbound

This paper cites Compressed sensing: From research to clinical practice with deep neural networks: Shortening scan times for magnetic resonance imaging,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Compressed sensing: From research to clinical practice with deep neural networks: Shortening scan times for magnetic resonance imaging,

Reference 5

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Observation f81d5101-1156-40e5-9b81-2058ac16172a · outbound

This paper cites Recent advances in parallel imaging for mri: Wave-caipi technique,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Recent advances in parallel imaging for mri: Wave-caipi technique,

Reference 6

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Observation 84c52881-1318-48eb-bdd6-6b8bc8759363 · outbound

This paper cites A review on accel- erated magnetic resonance imaging techniques: Parallel imaging, compressed sensing, and machine learning,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction A review on accel- erated magnetic resonance imaging techniques: Parallel imaging, compressed sensing, and machine learning,

Reference 7

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Observation 486ec862-1469-426f-a859-ef42ea34a98b · outbound

This paper cites An introduction to deep generative modeling,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction An introduction to deep generative modeling,

Reference 8

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Observation 2339a3be-937d-40d4-97e6-f8a6ad616506 · outbound

This paper cites A survey of multimodal deep generative models,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction A survey of multimodal deep generative models,

Reference 9

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Observation 2241abb4-52a4-4224-99a7-6217c16659f1 · outbound

This paper cites Mathematical models for magnetic resonance imaging reconstruction: An overview of the approaches, problems, and future research areas,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Mathematical models for magnetic resonance imaging reconstruction: An overview of the approaches, problems, and future research areas,

Reference 10

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Observation 273dfa1e-5edd-4238-9d57-9dc88664ca07 · outbound

This paper cites Dagan: Deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Dagan: Deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction,

Reference 11

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Observation 7841db65-a0f7-4bfe-aa77-ad5fd324873c · outbound

This paper cites Com- pressed sensing mri reconstruction using a generative adversarial network with a cyclic loss,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Com- pressed sensing mri reconstruction using a generative adversarial network with a cyclic loss,

Reference 12

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Observation ab0326bc-d668-4ba6-854c-e9d4b467255d · outbound

This paper cites High quality and fast compressed sensing mri reconstruction via edge-enhanced dual discriminator generative adversarial network,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction High quality and fast compressed sensing mri reconstruction via edge-enhanced dual discriminator generative adversarial network,

Reference 13

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Observation e08779dd-4816-4915-b15f-899e9de74129 · outbound

This paper cites Unpaired deep learning for accelerated mri using optimal transport driven cyclegan,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Unpaired deep learning for accelerated mri using optimal transport driven cyclegan,

Reference 14

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This paper cites Learning mri artefact removal with unpaired data,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Learning mri artefact removal with unpaired data,

Reference 15

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Observation a1bc0976-e5d5-4eea-889b-b790055b517f · outbound

This paper cites Time-dependent deep image prior for dynamic mri,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Time-dependent deep image prior for dynamic mri,

Reference 16

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This paper cites Accelerated mri with un-trained neural networks,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Accelerated mri with un-trained neural networks,

Reference 17

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Observation a21923a9-688c-418f-a025-727f28d2cce2 · outbound

This paper cites Nerp: implicit neural representation learning with prior embedding for sparsely sampled image reconstruction,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Nerp: implicit neural representation learning with prior embedding for sparsely sampled image reconstruction,

Reference 18

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Observation 908f83df-3f6a-466c-8b92-1deb3cae3607 · outbound

This paper cites Pearl: Cascaded self-supervised cross-fusion learning for parallel mri acceleration,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Pearl: Cascaded self-supervised cross-fusion learning for parallel mri acceleration,

Reference 19

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Observation 37a6d5ef-c166-48d2-bdd1-a9c7bcc048d9 · outbound

This paper cites Analysis of deep image prior and exploiting self-guidance for image reconstruction,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Analysis of deep image prior and exploiting self-guidance for image reconstruction,

Reference 20

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Observation de498dfb-ec53-4179-8a92-fdfea5e3efb0 · outbound

This paper cites The development of cognitive load theory: Replication crises and incorporation of other theories can lead to theory expansion,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction The development of cognitive load theory: Replication crises and incorporation of other theories can lead to theory expansion,

Reference 22

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Observation d418bc23-a3c2-4b6e-a8db-d0eb36760872 · outbound

This paper cites A cognitive load theory approach to defining and measuring task complexity through element interactivity,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction A cognitive load theory approach to defining and measuring task complexity through element interactivity,

Reference 23

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Observation 7eb1ffa7-8707-4247-837b-0b17e6a25495 · outbound

This paper cites Self-paced curriculum learning,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Self-paced curriculum learning,

Reference 24

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Observation b9b12ff9-c49a-4e77-a644-27005fcb09ef · outbound

This paper cites Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework,

Reference 25

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Observation 6428cba5-0440-455f-a9bb-a4c8e1b760d4 · outbound

This paper cites ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks

Reference 26

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Observation f4ee2143-3a62-4a32-b921-3584a66682c3 · outbound

This paper cites Brain-guided self- paced curriculum learning for adaptive human–machine interfaces,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Brain-guided self- paced curriculum learning for adaptive human–machine interfaces,

Reference 27

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This paper cites Phase retrieval using untrained neural network priors,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Phase retrieval using untrained neural network priors,

Reference 28

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Observation e22efd32-c7b9-426e-b0df-8e4da4be9e60 · outbound

This paper cites Deeptensor: Low-rank tensor decomposition with deep network priors,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Deeptensor: Low-rank tensor decomposition with deep network priors,

Reference 29

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This paper cites Denoising and regular- ization via exploiting the structural bias of convolutional generators,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Denoising and regular- ization via exploiting the structural bias of convolutional generators,

Reference 30

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Observation b4690849-6e42-4013-a4d2-00fd7a1415e4 · outbound

This paper cites Convergence guarantees of overparametrized wide deep inverse prior,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Convergence guarantees of overparametrized wide deep inverse prior,

Reference 31

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This paper cites Convergence and recovery guarantees of unsupervised neural networks for inverse problems,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Convergence and recovery guarantees of unsupervised neural networks for inverse problems,

Reference 32

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This paper cites Curriculum learning,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Curriculum learning,

Reference 33

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Observation 0d3bc8fc-a8e9-47e4-8fe3-b9f78bd5dc54 · outbound

This paper cites Self-paced learning for latent variable models,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Self-paced learning for latent variable models,

Reference 34

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This paper cites A theoretical understand- ing of self-paced learning,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction A theoretical understand- ing of self-paced learning,

Reference 35

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This paper cites Self-paced learning with diversity,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Self-paced learning with diversity,

Reference 36

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This paper cites Modl: Model- based deep learning architecture for inverse problems,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Modl: Model- based deep learning architecture for inverse problems,

Reference 37

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This paper cites fastmri: A publicly available raw k- space and dicom dataset of knee images for accelerated mr image reconstruction using machine learning,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction fastmri: A publicly available raw k- space and dicom dataset of knee images for accelerated mr image reconstruction using machine learning,

Reference 38

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Observation 6a63f8e1-2ac4-4683-8dd9-b2eabccef6b4 · outbound

This paper cites Incorporating reference in parallel imaging and compressed sensing,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Incorporating reference in parallel imaging and compressed sensing,

Reference 39

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This paper cites Image restora- tion using total variation regularized deep image prior,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Image restora- tion using total variation regularized deep image prior,

Reference 40

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This paper cites Decoupled algorithm for mri reconstruc- tion using nonlocal block matching model: Bm3d-mri,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Decoupled algorithm for mri reconstruc- tion using nonlocal block matching model: Bm3d-mri,

Reference 41

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This paper cites An unsupervised method for mri recovery: deep image prior with structured sparsity,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction An unsupervised method for mri recovery: deep image prior with structured sparsity,

Reference 42

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This paper cites Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data,

Reference 43

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This paper cites Image reconstruction via autoencoding sequential deep image prior,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Image reconstruction via autoencoding sequential deep image prior,

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This paper cites Spatiotemporal implicit neural representation for unsupervised dynamic mri re- construction,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Spatiotemporal implicit neural representation for unsupervised dynamic mri re- construction,

Reference 45

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This paper cites Learning interpretable decision rule sets: A submodular optimization approach,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Learning interpretable decision rule sets: A submodular optimization approach,

Reference 46

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This paper cites Data- efficient structured pruning via submodular optimization,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Data- efficient structured pruning via submodular optimization,

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This paper cites Greedy guarantees for minimum submodular cost submodular/non-submodular cover problem,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Greedy guarantees for minimum submodular cost submodular/non-submodular cover problem,

Reference 48

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This paper cites Weak submodularity implies localizability: Local search for constrained non- submodular function maximization,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Weak submodularity implies localizability: Local search for constrained non- submodular function maximization,

Reference 49

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This paper cites Neural tangent kernel: Convergence and generalization in neural net- works,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Neural tangent kernel: Convergence and generalization in neural net- works,

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This paper cites Neural Tangent Kernel: A Survey.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Neural Tangent Kernel: A Survey

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This paper cites Neural net- works can learn representations with gradient descent,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Neural net- works can learn representations with gradient descent,

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This paper cites Linear convergence of gradient and proximal-gradient methods under the polyak- lojasiewicz condition,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Linear convergence of gradient and proximal-gradient methods under the polyak- lojasiewicz condition,

Reference 53

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This paper cites A Generalized Alternating Method for Bilevel Learning under the Polyak-{\L}ojasiewicz Condition.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction A Generalized Alternating Method for Bilevel Learning under the Polyak-{\L}ojasiewicz Condition

Reference 54

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Observation f6a1f28e-f338-4159-9048-4fa7c94f955a · outbound

This paper cites Polyak-{\L}ojasiewicz inequality is es- sentially no more general than strong convexity for𝑐 2 functions,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Polyak-{\L}ojasiewicz inequality is es- sentially no more general than strong convexity for𝑐 2 functions,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-02T22:07:08.669881Z

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Observation 87a53eaf-71ed-43aa-8c71-838683754500 · outbound

This paper cites Range restricted iterative methods for linear discrete ill-posed problems,.

CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction Range restricted iterative methods for linear discrete ill-posed problems,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-02T22:07:08.703692Z

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