Paper Citation Record · LEDGER

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput

As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.18193.

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

pith.paper-citation-record.v1
2506.18193 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:26:57.657461Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy24
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c388f0b2-1b06-4380-8cf2-6d23c7125f04 · outbound

This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.450855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be30775a-80c1-4fa4-a002-b92f26803bb2 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput A simple framework for contrastive learning of visual representations

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.322084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7a0c6306-74e8-4d03-bce5-83dbc06ea3c1 · outbound

This paper cites Exploring simple siamese representation learning.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Exploring simple siamese representation learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.462358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 784545d4-a8b4-4037-8080-9da872fa051b · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Improved Baselines with Momentum Contrastive Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.467697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32e81805-7175-4a6d-b78e-1a813abbfcc7 · outbound

This paper cites Cover and Joy A.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Cover and Joy A

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.472885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08b738f5-a120-4607-9dc8-5670e287677a · outbound

This paper cites Deep residual learning for image recognition.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Deep residual learning for image recognition

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.477781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0a78dba-7271-4fda-bf92-07a8f3786f66 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Momentum contrast for unsupervised visual representation learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.488270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e82299ff-2db8-4652-b5f2-fdeed3903fee · outbound

This paper cites Realizing synchronized parameter updating, dynamic layer accumulation, and forward shortcuts in supervised contrastive parallel learning.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Realizing synchronized parameter updating, dynamic layer accumulation, and forward shortcuts in supervised contrastive parallel learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.263815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4dcd5582-e147-4f6c-b586-29347b88fb07 · outbound

This paper cites The vanishing gradient problem during learning recurrent neural nets and problem solutions.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput The vanishing gradient problem during learning recurrent neural nets and problem solutions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.247747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fdc4c9c2-c22a-452e-906b-5473b8124927 · outbound

This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.503717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2728d8a9-bb81-4295-b989-4564a9edbec6 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.509106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a6da181-4b7f-457d-a2c8-41d2e86e94b9 · outbound

This paper cites Decoupled neural interfaces using synthetic gradients.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Decoupled neural interfaces using synthetic gradients

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.205482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 242ffc12-a2b4-46ce-a093-c1d5d4d59350 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Scaling up visual and vision-language representation learning with noisy text supervision

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.189312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fe51f5de-4382-40eb-bb94-b50c88b6c61c · outbound

This paper cites Beyond data and model parallelism for deep neural networks.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Beyond data and model parallelism for deep neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.167616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 49fab7b8-4434-4fb6-841b-ee94661c9887 · outbound

This paper cites Understanding Dimensional Collapse in Contrastive Self-supervised Learning.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Understanding Dimensional Collapse in Contrastive Self-supervised Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.529622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ce60da5-11a2-4fbc-abcf-dfadacb3f077 · outbound

This paper cites Associated learning: Decomposing end-to-end backpropagation based on autoencoders and target propagation.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Associated learning: Decomposing end-to-end backpropagation based on autoencoders and target propagation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.149525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8bc937a6-9fcd-486e-8d5f-1a950da3c547 · outbound

This paper cites Supervised contrastive learning.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Supervised contrastive learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.541027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 153bf6f5-f95e-4bf9-a030-c85e0ef45a5d · outbound

This paper cites Deeply-supervised nets.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Deeply-supervised nets

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.118048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3af4fdac-b2c4-45fc-91d6-3dcdb32897d8 · outbound

This paper cites Self-organization in a perceptual network.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Self-organization in a perceptual network

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.099196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dee2e877-933f-4b7c-ba2a-112565c41671 · outbound

This paper cites Pipedream: Generalized pipeline parallelism for dnn training.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Pipedream: Generalized pipeline parallelism for dnn training

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.082012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 012f512a-8fc0-4a50-be4e-f0b8185696eb · outbound

This paper cites Training neural networks with local error signals.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Training neural networks with local error signals

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.066682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8ce6b912-27a8-43bb-8b4a-360fe840e197 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Representation Learning with Contrastive Predictive Coding

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.565665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22345f68-9e7b-4d74-86bd-dfc5ae86257f · outbound

This paper cites Self-supervised learning with an information maximization criterion.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Self-supervised learning with an information maximization criterion

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.051244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation df07c045-0e8e-455b-9968-7d1d510601a8 · outbound

This paper cites Glove: Global vectors for word representation.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Glove: Global vectors for word representation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.034829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 421aacf7-3a6b-47f2-bafb-b38ba406ec97 · outbound

This paper cites Learning transferable visual models from natural language supervision.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Learning transferable visual models from natural language supervision

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.579992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddb7adb3-55ea-4e58-8a38-dee92c250240 · outbound

This paper cites Measuring the effects of data parallelism on neural network training.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Measuring the effects of data parallelism on neural network training

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:58.004489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e10cd162-444b-4edc-b00a-b16ec53fb830 · outbound

This paper cites Spatiotemporal co-attention recurrent neural networks for human-skeleton motion prediction.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Spatiotemporal co-attention recurrent neural networks for human-skeleton motion prediction

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.988918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 60f1719e-bca3-491f-90f8-95f0c5214584 · outbound

This paper cites Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.973377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b9930b06-96f9-4454-a215-2bd38e38a9dc · outbound

This paper cites Blockwise Self-Supervised Learning at Scale.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Blockwise Self-Supervised Learning at Scale

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:26:57.734033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c61c2737-1340-41a6-8a3e-7f38716a5ee3 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.606756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cd33261-1e28-45a8-8abe-f2de59e8647c · outbound

This paper cites Going deeper with convolutions.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Going deeper with convolutions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.611920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6bff137-6afd-471f-b044-cf033e9009fa · outbound

This paper cites Coherence constrained graph lstm for group activity recognition.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Coherence constrained graph lstm for group activity recognition

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.946542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c3e1a715-e5e5-4e38-926e-26cca7277c3d · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Branchynet: Fast inference via early exiting from deep neural networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.929597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7339ba12-af84-48d9-b96b-df14e369345b · outbound

This paper cites On Mutual Information Maximization for Representation Learning.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput On Mutual Information Maximization for Representation Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:57.626429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df8cb2d5-0da2-4f9a-9c18-2ec3e0f0a52f · outbound

This paper cites Decomposing end-to-end backpropagation based on SCPL.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Decomposing end-to-end backpropagation based on SCPL

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.912169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e01b6e66-013f-4979-b7ca-20e6d397fc93 · outbound

This paper cites Revisiting locally supervised learning: an alternative to end-to-end training.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Revisiting locally supervised learning: an alternative to end-to-end training

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.891822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd0c88f0-a2e2-447d-9e7c-ebffa07a0316 · outbound

This paper cites Associated learning: an alternative to end-to-end backpropagation that works on cnn, rnn, and transformer.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Associated learning: an alternative to end-to-end backpropagation that works on cnn, rnn, and transformer

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.874035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3eebe8f7-85dd-422c-b572-47c69195dd76 · outbound

This paper cites Higcin: Hierarchical graph-based cross inference network for group activity recognition.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Higcin: Hierarchical graph-based cross inference network for group activity recognition

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.857346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d791d001-0a9f-4450-9f21-9334a589695d · outbound

This paper cites Towards interpretable deep local learning with successive gradient reconciliation.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Towards interpretable deep local learning with successive gradient reconciliation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.840799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 51fa7bcc-d9b0-467a-8985-0f9edb92ece5 · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction.

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput Barlow twins: Self-supervised learning via redundancy reduction

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:26:57.821790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.