Paper Citation Record · LEDGER

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection

As of 20 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2506.10200.

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

pith.paper-citation-record.v1
2506.10200 v1

Coverage vector

measured 47 of 47 reference resolution

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measured 47 of 47 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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Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 69ba86be-6291-4309-89ab-14f1dc21c16c · outbound

This paper cites k-means++: The advantages of careful seeding.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection k-means++: The advantages of careful seeding

Reference 1

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Observation f46a3092-5435-4634-892c-423e9e420f1b · outbound

This paper cites Density-based clustering over an evolving data stream with noise.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Density-based clustering over an evolving data stream with noise

Reference 2

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Observation 60f4e040-8add-4faf-89d7-fdcec4dc770c · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Unsupervised learning of visual features by contrasting cluster assignments

Reference 3

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Observation 9d75594c-ed1e-4b64-be5c-25dd928fddb0 · outbound

This paper cites Probabilistic machine learning for healthcare.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Probabilistic machine learning for healthcare

Reference 4

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Observation 8f8daf6d-5e1e-4a86-bf41-3b6d9d01bd6b · outbound

This paper cites Maximum likelihood from incomplete data via the em algorithm.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Maximum likelihood from incomplete data via the em algorithm

Reference 5

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Observation 165be385-d6f9-4ab4-b73f-221f6c2a1703 · outbound

This paper cites Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

Reference 6

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Observation a5003367-7860-4f14-a2b2-96200ec6d03e · outbound

This paper cites Optimal representations for covariate shifts.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Optimal representations for covariate shifts

Reference 7

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Observation 3e39023e-07d5-43ec-8b7b-af5f4acd8821 · outbound

This paper cites Cadet: Fully self-supervised out-of-distribution detection with contrastive learning.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Cadet: Fully self-supervised out-of-distribution detection with contrastive learning

Reference 8

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Observation 3cd879b7-4e7c-444f-bfe9-42f644ef76a0 · outbound

This paper cites Deep residual learning for image recognition.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Deep residual learning for image recognition

Reference 9

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Observation 5cdc324a-4650-49d8-a437-f282663207aa · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 10

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Observation 16c4ccc7-d9d1-4ef8-9cf3-e290609f75fa · outbound

This paper cites Approximating the kullback leibler divergence between gaussian mixture models.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Approximating the kullback leibler divergence between gaussian mixture models

Reference 11

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Observation 44db1152-f9fb-4645-9868-78143a28b7ab · outbound

This paper cites Comparing partitions.Journal of classification, 2:193–218, 1985.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Comparing partitions.Journal of classification, 2:193–218, 1985

Reference 12

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Observation c115481e-50bd-4ac3-b0f4-6055844d0082 · outbound

This paper cites Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering

Reference 13

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Observation 77fbb605-25d8-4365-bdbb-e8b13033256d · outbound

This paper cites An introduc- tion to variational methods for graphical models.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection An introduc- tion to variational methods for graphical models

Reference 14

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Observation 4b923094-ae23-4c24-970d-4ada3ca9eed6 · outbound

This paper cites Rodd: A self- supervised approach for robust out-of-distribution detection.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Rodd: A self- supervised approach for robust out-of-distribution detection

Reference 15

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Observation 542cd8d2-915a-43ad-8b30-b15b8727645b · outbound

This paper cites Semi-supervised learning with deep generative models.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Semi-supervised learning with deep generative models

Reference 16

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Observation 00535fe9-f84f-4ccb-a336-8d5e737a96d4 · outbound

This paper cites Learning multiple layers of features from tiny images.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Learning multiple layers of features from tiny images

Reference 17

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Observation 7fe37c40-a7de-49a3-95b2-fc0a8cef47b6 · outbound

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

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Gradient-based learning applied to document recognition

Reference 18

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Observation 8c68855e-a30f-416c-bfed-3af8bee4be22 · outbound

This paper cites Fast decision boundary based out-of-distribution detector.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Fast decision boundary based out-of-distribution detector

Reference 19

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Observation 1363bf17-54ec-4975-876a-6ff3c11110ed · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 20

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Observation 5aeb1d49-5bea-4d18-b181-ee55e19bde43 · outbound

This paper cites Representa- tional continuity for unsupervised continual learning.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Representa- tional continuity for unsupervised continual learning

Reference 21

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Observation 9a5ca2f5-6ec1-4867-ba7d-3f0eba044fe9 · outbound

This paper cites A clinician’s guide to understanding bias in critical clinical prediction models.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection A clinician’s guide to understanding bias in critical clinical prediction models

Reference 22

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Observation e108780c-cec1-4470-865f-47c6311af136 · outbound

This paper cites Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization

Reference 23

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Observation 3e598804-d86f-4cec-8598-4b4454371f52 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Reading digits in natural images with unsupervised feature learning

Reference 24

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Observation f38f8610-c32a-4c79-880c-1805c17bf093 · outbound

This paper cites Scikit- learn: Machine learning in python.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Scikit- learn: Machine learning in python

Reference 25

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This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 26

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DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Deep clustering: A comprehensive survey

Reference 27

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Observation ef9934f3-47cd-443c-98c8-1e60b931cf5a · outbound

This paper cites Mcluster- vaes: an end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Mcluster- vaes: an end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data

Reference 28

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Observation f3245e0a-9e81-493d-b584-c6a36e17dbb9 · outbound

This paper cites Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Reference 29

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Observation 242ba8ae-75f7-489e-9ab5-41f3244efbbd · outbound

This paper cites Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation

Reference 30

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Observation 86b6baf3-5d27-472c-84dc-2fc9b0c4684b · outbound

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

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Facenet: A unified embedding for face recognition and clustering

Reference 31

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DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Deep residual learning for image recognition: A survey

Reference 32

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Observation aff192e7-7cfe-4706-b469-c1012d535f6e · outbound

This paper cites How robust is unsupervised representation learning to distribution shift? In The Eleventh International Conference on Learning Representations, 2023.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection How robust is unsupervised representation learning to distribution shift? In The Eleventh International Conference on Learning Representations, 2023

Reference 33

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Observation 588d5697-19c0-41b7-b6cf-70c57fc51212 · outbound

This paper cites Modern information retrieval: A brief overview.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Modern information retrieval: A brief overview

Reference 34

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Observation 7568c066-f6eb-4ef0-a426-fea0f635b616 · outbound

This paper cites A Survey on Open-Set Image Recognition.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection A Survey on Open-Set Image Recognition

Reference 35

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DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Dice: Leveraging sparsification for out-of-distribution detection

Reference 36

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Observation 16e0f095-efc7-4265-ae72-77bebb1e50ef · outbound

This paper cites Out-of-distribution detection with deep nearest neighbors.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Out-of-distribution detection with deep nearest neighbors

Reference 37

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Observation 45761819-a7f7-4ede-9041-d247927c33cf · outbound

This paper cites Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions

Reference 38

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This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 39

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This paper cites Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:51.344699Z

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Observation ea489ecb-7327-4f46-8df2-24bfee863969 · outbound

This paper cites Scaling for training time and post-hoc out-of-distribution detection enhancement.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Scaling for training time and post-hoc out-of-distribution detection enhancement

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:51.102954Z

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Observation 747bf8f9-32a9-4d6a-878c-bff8d3e0c1e3 · outbound

This paper cites Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis

Reference 42

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This paper cites Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification

Reference 43

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DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Generalized out-of-distribution detection: A survey

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:50.895308Z

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Observation 74120b64-8914-4342-941f-aea130003a49 · outbound

This paper cites Out-Of-Distribution Detection with Diversification (Provably).

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Out-Of-Distribution Detection with Diversification (Provably)

Reference 45

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Observation 4396e736-3602-4aa1-9aa7-a7b89f4ee344 · outbound

This paper cites Out-of-distribution detection for medical applications: Guidelines for practical evaluation.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection Out-of-distribution detection for medical applications: Guidelines for practical evaluation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:50.760576Z

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Observation c6257834-1f6e-4fd1-828b-8ee3705d771a · outbound

This paper cites OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection.

DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection

Reference 47

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Pith citing papers

No inbound Pith citation observations are available.