Paper Citation Record · LEDGER

Tutorial: VAE as an inference paradigm for neuroimaging

As of 14 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2501.08009.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.08009 v1

Coverage vector

measured 46 of 46 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-10T20:32:48.765272Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 20427b13-5fc0-46ef-a283-67c1386b7a80 · outbound

This paper cites Auto-Encoding Variational Bayes.

Tutorial: VAE as an inference paradigm for neuroimaging Auto-Encoding Variational Bayes

Reference 1

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Observation 5ccf0844-7a17-48be-90d4-67732561a38f · outbound

This paper cites Stochastic backpropagation and approximate inference in deep generative models.

Tutorial: VAE as an inference paradigm for neuroimaging Stochastic backpropagation and approximate inference in deep generative models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.471200Z

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Observation 9e520e32-5855-4851-a675-df03126b7b70 · outbound

This paper cites Weight uncertainty in neural network.

Tutorial: VAE as an inference paradigm for neuroimaging Weight uncertainty in neural network

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.455838Z

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Observation cd03b1a2-e422-4584-bce1-f1c5128d1a3f · outbound

This paper cites Long-term neural and physiological phenotyping of a single human.

Tutorial: VAE as an inference paradigm for neuroimaging Long-term neural and physiological phenotyping of a single human

Reference 4

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Observation c0ea768f-0230-4d89-8c0f-d7124d2c8be8 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Tutorial: VAE as an inference paradigm for neuroimaging Adam: A Method for Stochastic Optimization

Reference 5

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Observation 012d7057-5d21-4a87-825e-2d7723177f4b · outbound

This paper cites Automatic differentiation variational inference.

Tutorial: VAE as an inference paradigm for neuroimaging Automatic differentiation variational inference

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.422818Z

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Observation ddeadbad-99ac-4b80-aa82-27d6652d355c · outbound

This paper cites Machine learning for neuroimaging with scikit-learn.

Tutorial: VAE as an inference paradigm for neuroimaging Machine learning for neuroimaging with scikit-learn

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.407126Z

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Observation db389357-a54e-4bc4-b88e-30ff9303e17f · outbound

This paper cites The cognitive neuroscience of working memory.

Tutorial: VAE as an inference paradigm for neuroimaging The cognitive neuroscience of working memory

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.391816Z

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Observation 40f80d28-2a6e-4ffc-95b2-1ca7a248bf98 · outbound

This paper cites The kdd process for extracting useful knowledge from volumes of data.

Tutorial: VAE as an inference paradigm for neuroimaging The kdd process for extracting useful knowledge from volumes of data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.375416Z

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Observation 8c76c03f-48d7-4217-814a-7b221a0cadd6 · outbound

This paper cites A global geometric framework for nonlinear dimensionality reduction.

Tutorial: VAE as an inference paradigm for neuroimaging A global geometric framework for nonlinear dimensionality reduction

Reference 10

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Observation bb5fd73f-283a-465a-a95a-9e81bd58c1d3 · outbound

This paper cites Reducing the dimension- ality of data with neural networks.

Tutorial: VAE as an inference paradigm for neuroimaging Reducing the dimension- ality of data with neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.349684Z

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Observation 3ef542fa-2f51-4e7d-993a-82f5cd905843 · outbound

This paper cites Generative adversarial nets.

Tutorial: VAE as an inference paradigm for neuroimaging Generative adversarial nets

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.332018Z

Source-reported events for the cited work

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Observation eefd44c4-e389-45e1-ad5c-6e49ac4b38d2 · outbound

This paper cites High-resolution image synthesis with latent diffusion mod- els.

Tutorial: VAE as an inference paradigm for neuroimaging High-resolution image synthesis with latent diffusion mod- els

Reference 13

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Observation ea2964a9-07fd-48df-b3a6-57d22f81c5c3 · outbound

This paper cites Modern multidimensional scaling: Theory and applications.

Tutorial: VAE as an inference paradigm for neuroimaging Modern multidimensional scaling: Theory and applications

Reference 14

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Observation 1f0d5f13-ead4-4ddf-8ed0-f9db91189c47 · outbound

This paper cites Nonlinear dimensionality reduction by locally linear embedding.

Tutorial: VAE as an inference paradigm for neuroimaging Nonlinear dimensionality reduction by locally linear embedding

Reference 15

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Observation c2672eb7-5c16-4043-9271-83dcbc2eb31a · outbound

This paper cites Deep learning.

Tutorial: VAE as an inference paradigm for neuroimaging Deep learning

Reference 16

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Observation 580bb75f-1781-41b6-bb78-88006d483a22 · outbound

This paper cites Deep residual learning for image recognition.

Tutorial: VAE as an inference paradigm for neuroimaging Deep residual learning for image recognition

Reference 17

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Observation 17752540-2a2d-43fe-8533-1563597941ee · outbound

This paper cites Cross-orientation suppression in visual area v2.

Tutorial: VAE as an inference paradigm for neuroimaging Cross-orientation suppression in visual area v2

Reference 18

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Observation 98d1bcae-5596-4524-a71c-f7d26a2703fa · outbound

This paper cites On information and sufficiency.

Tutorial: VAE as an inference paradigm for neuroimaging On information and sufficiency

Reference 19

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Observation 59b2b707-6d34-4134-b93a-07647e029f2d · outbound

This paper cites What’s wrong with mean-squared error? In Digital images and human vision , pages 207–220.

Tutorial: VAE as an inference paradigm for neuroimaging What’s wrong with mean-squared error? In Digital images and human vision , pages 207–220

Reference 20

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Observation 14cdab3c-024e-46cb-8d98-36f281570a26 · outbound

This paper cites Image quality measures and their performance.

Tutorial: VAE as an inference paradigm for neuroimaging Image quality measures and their performance

Reference 21

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Observation a506662a-8272-4272-95c1-64b5821ec99b · outbound

This paper cites A universal image quality index.

Tutorial: VAE as an inference paradigm for neuroimaging A universal image quality index

Reference 22

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Observation d74423e5-eda6-492b-8058-8dddb5708fe7 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Tutorial: VAE as an inference paradigm for neuroimaging Image quality assessment: from error visibility to structural similarity

Reference 23

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Observation 890557fb-be69-4108-b648-a80c127bea4a · outbound

This paper cites A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference.

Tutorial: VAE as an inference paradigm for neuroimaging A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

Reference 24

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Observation 11fc51b7-3e1e-4797-b625-7910dcb3926c · outbound

This paper cites Facenet: A unified embedding for face recognition and clustering.

Tutorial: VAE as an inference paradigm for neuroimaging Facenet: A unified embedding for face recognition and clustering

Reference 25

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Observation 90db97a8-d0f1-49ad-a964-00e11d9666ca · outbound

This paper cites You only look once: Unified, real-time object detection.

Tutorial: VAE as an inference paradigm for neuroimaging You only look once: Unified, real-time object detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.153716Z

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Observation 590e5940-8195-4304-bf15-f422772ea01d · outbound

This paper cites U-net: Convolu- tional networks for biomedical image segmentation.

Tutorial: VAE as an inference paradigm for neuroimaging U-net: Convolu- tional networks for biomedical image segmentation

Reference 27

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Observation ea944169-f55f-458b-ade8-ec64aaada6ed · outbound

This paper cites A survey on deep learning in medical image analysis.

Tutorial: VAE as an inference paradigm for neuroimaging A survey on deep learning in medical image analysis

Reference 28

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Observation 5a0b0ae6-f4df-4acf-b12b-ccc90db5e94b · outbound

This paper cites Monte carlo statistical methods, 1999.

Tutorial: VAE as an inference paradigm for neuroimaging Monte carlo statistical methods, 1999

Reference 29

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Observation 3b982475-8d91-48b8-b9af-a31b4c30930a · outbound

This paper cites An introduction to variational autoencoders.

Tutorial: VAE as an inference paradigm for neuroimaging An introduction to variational autoencoders

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.095933Z

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Observation ea3175c9-3cf0-43af-9bd9-932d2ffb0140 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Tutorial: VAE as an inference paradigm for neuroimaging Pytorch: An imperative style, high-performance deep learning library

Reference 31

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Observation 81926834-5ea2-4ba3-b616-f26f3f8c1715 · outbound

This paper cites Multilayer feedfor- ward networks are universal approximators.Neural networks, 2(5):359–366, 1989.

Tutorial: VAE as an inference paradigm for neuroimaging Multilayer feedfor- ward networks are universal approximators.Neural networks, 2(5):359–366, 1989

Reference 32

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Observation 92140b1f-0126-491b-b2fe-672c906fe821 · outbound

This paper cites Neural networks and the bias/variance dilemma.

Tutorial: VAE as an inference paradigm for neuroimaging Neural networks and the bias/variance dilemma

Reference 33

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This paper cites Variational Lossy Autoencoder.

Tutorial: VAE as an inference paradigm for neuroimaging Variational Lossy Autoencoder

Reference 34

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This paper cites InfoVAE: Information Maximizing Variational Autoencoders.

Tutorial: VAE as an inference paradigm for neuroimaging InfoVAE: Information Maximizing Variational Autoencoders

Reference 35

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Observation 0b5d9d0c-32e7-48ba-8a0b-a25747dc11fc · outbound

This paper cites Understanding deep learning (still) requires rethinking generaliza- tion.

Tutorial: VAE as an inference paradigm for neuroimaging Understanding deep learning (still) requires rethinking generaliza- tion

Reference 36

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Observation e8a6021a-8ed6-4fc0-b78c-cb2be6bcc976 · outbound

This paper cites On the importance of initialization and momentum in deep learning.

Tutorial: VAE as an inference paradigm for neuroimaging On the importance of initialization and momentum in deep learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:49.015435Z

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Observation 9d1b38a9-6bea-4df7-9336-b9d1ec88d427 · outbound

This paper cites Deep learning and the information bottleneck principle.

Tutorial: VAE as an inference paradigm for neuroimaging Deep learning and the information bottleneck principle

Reference 38

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This paper cites Please help! i’m stuck in a local minimum and how i climbed out, 2024.

Tutorial: VAE as an inference paradigm for neuroimaging Please help! i’m stuck in a local minimum and how i climbed out, 2024

Reference 39

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Observation c3bb22e8-267a-44c8-80c2-ca88eda5ff4f · outbound

This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework.

Tutorial: VAE as an inference paradigm for neuroimaging beta-vae: Learning basic visual concepts with a constrained variational framework

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:48.971387Z

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Observation 5f0cfb2d-b356-476d-941f-7150d549211f · outbound

This paper cites Fusing multimodal neuroimaging data with a varia- tional autoencoder.

Tutorial: VAE as an inference paradigm for neuroimaging Fusing multimodal neuroimaging data with a varia- tional autoencoder

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:48.955766Z

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Observation 58ccbd89-c5da-47c4-91df-e6342e39d4f2 · outbound

This paper cites A cross-modality latent representation for the prediction of clinical symptomatology in parkinson’s disease.

Tutorial: VAE as an inference paradigm for neuroimaging A cross-modality latent representation for the prediction of clinical symptomatology in parkinson’s disease

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:48.939984Z

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Observation a1bf9a52-14b3-48aa-89cf-27050d470152 · outbound

This paper cites Deep variational autoencoder for mapping functional brain networks.

Tutorial: VAE as an inference paradigm for neuroimaging Deep variational autoencoder for mapping functional brain networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:48.924286Z

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Observation afde3dd8-3d0c-433a-ac4b-1f94a23bea2c · outbound

This paper cites An image feature mapping model for continuous longitudinal data completion and generation of syn- thetic patient trajectories.

Tutorial: VAE as an inference paradigm for neuroimaging An image feature mapping model for continuous longitudinal data completion and generation of syn- thetic patient trajectories

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:48.908130Z

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Observation 82558487-031f-40b3-8d2f-4de365dc0a1d · outbound

This paper cites The concentration of measure phenomenon.

Tutorial: VAE as an inference paradigm for neuroimaging The concentration of measure phenomenon

Reference 45

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This paper cites High-dimensional data analysis: The curses and blessings of dimensionality.

Tutorial: VAE as an inference paradigm for neuroimaging High-dimensional data analysis: The curses and blessings of dimensionality

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:32:48.881613Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.