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Paper Citation Record · LEDGER

A Principled Framework for Multi-View Contrastive Learning

As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2507.06979.

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

pith.paper-citation-record.v1
2507.06979 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:02:14.437243Z

measured 52 of 52 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.

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

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation bc0a47fd-9e83-4503-a627-56a875aa3e4c · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere,.

A Principled Framework for Multi-View Contrastive Learning Understanding contrastive representation learning through alignment and uniformity on the hypersphere,

Reference 1

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Observation 487264dc-2627-4eea-b00a-5324f8714ad8 · outbound

This paper cites Learning representations by maximizing mutual information across views,.

A Principled Framework for Multi-View Contrastive Learning Learning representations by maximizing mutual information across views,

Reference 2

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Observation 9842ecbf-b7d2-45de-9726-9361e80212dc · outbound

This paper cites Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error.

A Principled Framework for Multi-View Contrastive Learning Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error

Reference 3

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Observation 890e70a4-64f9-4d90-9142-96dd29d94eb9 · outbound

This paper cites Augment your batch: Improving generalization through instance repeti- tion,.

A Principled Framework for Multi-View Contrastive Learning Augment your batch: Improving generalization through instance repeti- tion,

Reference 4

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Observation d0cfaa85-f2d5-47cc-9508-9b47bb221ff1 · outbound

This paper cites The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective,.

A Principled Framework for Multi-View Contrastive Learning The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective,

Reference 5

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Observation 8314b134-59e2-48b3-8b94-02529bb4749c · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assign- ments,.

A Principled Framework for Multi-View Contrastive Learning Unsupervised learning of visual features by contrasting cluster assign- ments,

Reference 6

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Observation 26d5b057-395f-4fca-833c-9e9db129bb17 · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

A Principled Framework for Multi-View Contrastive Learning Emerging properties in self-supervised vision transformers,

Reference 7

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Observation 95174f07-9652-4ada-9fed-24be24fce467 · outbound

This paper cites Vicregl: Self-supervised learning of local visual features,.

A Principled Framework for Multi-View Contrastive Learning Vicregl: Self-supervised learning of local visual features,

Reference 8

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Observation 290ece9f-a986-4ad1-bc66-50f91e7113e5 · outbound

This paper cites Contrastive multiview coding,.

A Principled Framework for Multi-View Contrastive Learning Contrastive multiview coding,

Reference 9

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Observation f390be64-6339-4609-8b4e-eb55a3f894b5 · outbound

This paper cites Poly-view contrastive learning,.

A Principled Framework for Multi-View Contrastive Learning Poly-view contrastive learning,

Reference 10

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Observation d2db9797-93c6-46f6-899f-8516443eb28a · outbound

This paper cites Bridging mini-batch and asymptotic analysis in contrastive learning: From infoNCE to kernel-based losses,.

A Principled Framework for Multi-View Contrastive Learning Bridging mini-batch and asymptotic analysis in contrastive learning: From infoNCE to kernel-based losses,

Reference 11

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Observation 52aff91c-a2b3-4ac2-86ce-5ad1205e03db · outbound

This paper cites Understanding dimensional collapse in contrastive self-supervised learning,.

A Principled Framework for Multi-View Contrastive Learning Understanding dimensional collapse in contrastive self-supervised learning,

Reference 12

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Observation 92901114-9b1b-4a80-aefd-a69fecfa49f8 · outbound

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

A Principled Framework for Multi-View Contrastive Learning Learning transferable visual models from natural language supervision,

Reference 13

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Observation fb0219a0-cdbb-47f4-bd14-d4772dcc84cd · outbound

This paper cites Tricolo: Trimodal contrastive loss for text to shape retrieval,.

A Principled Framework for Multi-View Contrastive Learning Tricolo: Trimodal contrastive loss for text to shape retrieval,

Reference 14

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Observation d2b67a94-c88a-43a1-b9c8-4afa5f82372a · outbound

This paper cites Contrastive multimodal fusion with tupleinfonce,.

A Principled Framework for Multi-View Contrastive Learning Contrastive multimodal fusion with tupleinfonce,

Reference 15

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Observation bfc30664-9b92-48dd-ae9e-b9a308f04a73 · outbound

This paper cites Contextual augmented global contrast for multimodal intent recognition,.

A Principled Framework for Multi-View Contrastive Learning Contextual augmented global contrast for multimodal intent recognition,

Reference 16

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Observation 6b945324-f172-4099-970c-69cee2e58f5a · outbound

This paper cites Learning a similarity metric discriminatively, with application to face verification,.

A Principled Framework for Multi-View Contrastive Learning Learning a similarity metric discriminatively, with application to face verification,

Reference 17

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Observation 31956479-74c2-4fee-9947-f6a8db5699b3 · outbound

This paper cites Improved deep metric learning with multi-class n-pair loss objective,.

A Principled Framework for Multi-View Contrastive Learning Improved deep metric learning with multi-class n-pair loss objective,

Reference 18

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Observation f0b6f4aa-0c6c-41ce-bd79-dcc960fe60ac · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

A Principled Framework for Multi-View Contrastive Learning Representation Learning with Contrastive Predictive Coding

Reference 19

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Observation c4b21919-cf78-4750-a354-be8f7762d403 · outbound

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

A Principled Framework for Multi-View Contrastive Learning A simple framework for contrastive learning of visual representations,

Reference 20

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Observation 0af9efc3-38ed-4cd5-945b-17f48c616b75 · outbound

This paper cites With a little help from my friends: Nearest-neighbor contrastive learning of visual representations,.

A Principled Framework for Multi-View Contrastive Learning With a little help from my friends: Nearest-neighbor contrastive learning of visual representations,

Reference 21

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Observation 3e47573c-57e7-4d07-a5a4-2766fba508ce · outbound

This paper cites Decoupled contrastive learning,.

A Principled Framework for Multi-View Contrastive Learning Decoupled contrastive learning,

Reference 22

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Observation c336da5a-5f63-4fb3-8fbc-1e1f4d8df3d6 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

A Principled Framework for Multi-View Contrastive Learning Momentum contrast for unsupervised visual representation learning,

Reference 23

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Observation c03ea183-6810-4047-8831-09a80fa23b54 · outbound

This paper cites Contrastive learning with hard negative samples,.

A Principled Framework for Multi-View Contrastive Learning Contrastive learning with hard negative samples,

Reference 24

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Observation 33b3cf60-5414-448f-890b-57de8141381c · outbound

This paper cites On feature decorrelation in self-supervised learning,.

A Principled Framework for Multi-View Contrastive Learning On feature decorrelation in self-supervised learning,

Reference 25

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Observation d344dc1f-a96f-4262-b79f-7300882a38a8 · outbound

This paper cites Understanding dimensional collapse in contrastive self-supervised learning,.

A Principled Framework for Multi-View Contrastive Learning Understanding dimensional collapse in contrastive self-supervised learning,

Reference 26

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Observation 26fe4c9c-d67b-4cf8-9686-8c929e390af6 · outbound

This paper cites How much data are augmentations worth? an investigation into scaling laws, invariance, and implicit regularization,.

A Principled Framework for Multi-View Contrastive Learning How much data are augmentations worth? an investigation into scaling laws, invariance, and implicit regularization,

Reference 27

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Observation ec8b8fa0-3107-4d17-af7b-c00b1841bf1a · outbound

This paper cites Grounding inductive biases in natural images: invariance stems from variations in data,.

A Principled Framework for Multi-View Contrastive Learning Grounding inductive biases in natural images: invariance stems from variations in data,

Reference 28

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Observation d5b9cd6d-c2ed-489a-b228-0ffa612ec291 · outbound

This paper cites EMP-SSL: Towards Self-Supervised Learning in One Training Epoch.

A Principled Framework for Multi-View Contrastive Learning EMP-SSL: Towards Self-Supervised Learning in One Training Epoch

Reference 29

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Observation 1587bc72-02ab-434d-8e78-afa956907489 · outbound

This paper cites Whitening for self-supervised representation learning,.

A Principled Framework for Multi-View Contrastive Learning Whitening for self-supervised representation learning,

Reference 30

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Observation fee42788-4be4-497f-beed-5d763c7718f5 · outbound

This paper cites Adaptive multi-head contrastive learning,.

A Principled Framework for Multi-View Contrastive Learning Adaptive multi-head contrastive learning,

Reference 31

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Observation 98ed067d-f483-42bf-9bba-84d7c77662db · outbound

This paper cites From global to local: Multi-patch and multi-scale contrastive similarity learning for unsupervised defocus blur detection,.

A Principled Framework for Multi-View Contrastive Learning From global to local: Multi-patch and multi-scale contrastive similarity learning for unsupervised defocus blur detection,

Reference 32

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Observation 162f87fc-2b6a-45f2-8518-187f37b62a08 · outbound

This paper cites Multi- view action recognition using contrastive learning,.

A Principled Framework for Multi-View Contrastive Learning Multi- view action recognition using contrastive learning,

Reference 33

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Observation 6003f504-3b3a-45d3-b8ec-c94e760224af · outbound

This paper cites Multi-level feature learning for contrastive multi-view clustering,.

A Principled Framework for Multi-View Contrastive Learning Multi-level feature learning for contrastive multi-view clustering,

Reference 34

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Observation ef2bf66f-5ddc-4103-8d15-5089584690af · outbound

This paper cites Contrasting multiple representations with the multi-marginal matching gap,.

A Principled Framework for Multi-View Contrastive Learning Contrasting multiple representations with the multi-marginal matching gap,

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.373173Z digest=sha256:a2feb2ce8079cc2a11865da4beb002530cdce39736101ea59237102a929fc7cd

Observation 59e92252-0379-49b5-8097-1f50483bc275 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning,.

A Principled Framework for Multi-View Contrastive Learning Bootstrap your own latent-a new approach to self-supervised learning,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.689175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.376846Z digest=sha256:4ae3425d8dd2d07005186db1b378c93ed4fe84f141bfbc115615c09526c56fb3

Observation d640d37d-e86e-49f1-85f6-fd5457cf2bb5 · outbound

This paper cites Fastsiam: Resource- efficient self-supervised learning on a single gpu,.

A Principled Framework for Multi-View Contrastive Learning Fastsiam: Resource- efficient self-supervised learning on a single gpu,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.677190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.380480Z digest=sha256:4c9aa0dc266a26f2c6bdecc12a2bbd5758ee5b30e746ebe31818e13b63404604

Observation 983abdf1-b0f9-43c1-a18a-a9325ceb5d9d · outbound

This paper cites Unsupervised feature learning by cross- level instance-group discrimination,.

A Principled Framework for Multi-View Contrastive Learning Unsupervised feature learning by cross- level instance-group discrimination,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.665065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.383875Z digest=sha256:01d3df452afb238d4547dfe3624917f48488c31367d84a33ffdc9e49618a0a44

Observation d84a2323-8e68-4716-bc0d-dd4e511cb823 · outbound

This paper cites Dual temperature helps contrastive learning without many negative samples: Towards understanding and simplifying moco,.

A Principled Framework for Multi-View Contrastive Learning Dual temperature helps contrastive learning without many negative samples: Towards understanding and simplifying moco,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.652525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.387445Z digest=sha256:3ea5bf060587cb5210ff69fc87efed4a038764701be61934d543ab510c9d62b4

Observation 95e96c4a-3280-41ec-a3ff-641c08a72067 · outbound

This paper cites Unsupervised feature learning via non-parametric instance discrimination,.

A Principled Framework for Multi-View Contrastive Learning Unsupervised feature learning via non-parametric instance discrimination,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.640721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.390930Z digest=sha256:a6605de05b583c151a9fc5c90e44a3b4ec88558bc62d043c6f41eee6f8d17d04

Observation f692fc97-591d-43b0-b091-aadf34845f84 · outbound

This paper cites Multizoo & multibench: A standardized toolkit for multimodal deep learning,.

A Principled Framework for Multi-View Contrastive Learning Multizoo & multibench: A standardized toolkit for multimodal deep learning,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.628676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.394312Z digest=sha256:a6a7ee8d6205b06f8a117ae204704b54ef6fe7773ba1b3bfdd0e78cb3a753db7

Observation 54366bbe-3655-488c-a447-19de8e49e231 · outbound

This paper cites M-sena: An integrated platform for multimodal sentiment analysis,.

A Principled Framework for Multi-View Contrastive Learning M-sena: An integrated platform for multimodal sentiment analysis,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.617829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.397789Z digest=sha256:894b03008b4f9eb1b2e3a1af05e45b27b0a0dd42d673f05e8e307b30117dae2b

Observation 56767095-2aa5-42c1-aec4-b68594fba3ba · outbound

This paper cites Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph,.

A Principled Framework for Multi-View Contrastive Learning Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.606074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.401402Z digest=sha256:4c71d29c52dc63937c43cb03ea291ce9d3935e7b7c07b392f7e455efad06432c

Observation 3954f81b-8cb8-457e-a98e-5e598c73d026 · outbound

This paper cites Ch- sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality,.

A Principled Framework for Multi-View Contrastive Learning Ch- sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality,

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T19:02:14.406061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:02:14.406061Z digest=sha256:f0a36ef695e8e6b04cdfaaf098e973963806164e060db8b653b27433c3d2bb44

Observation a6105407-85ed-4c4c-bb42-8cf8048a8a8e · outbound

This paper cites Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition,.

A Principled Framework for Multi-View Contrastive Learning Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.588222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.409605Z digest=sha256:9840d31601420e00c4163e0d85972f890d05da8248b5bf385cd8ee4746d591b2

Observation 46b63d65-ed5d-44bf-8325-b5853d12037d · outbound

This paper cites Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank,.

A Principled Framework for Multi-View Contrastive Learning Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.576503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.413348Z digest=sha256:746ece1cb7ca6dc3c9174d255128f825aeb86ea9939c7cd1336f13508eb961ac

Observation b225beb6-03f3-48a9-9087-773fcd2543b2 · outbound

This paper cites MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition.

A Principled Framework for Multi-View Contrastive Learning MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition

Reference 47

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:02:14.478376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.417015Z digest=sha256:9475fca57da75d97a11a6e586bf3ce1ceb6aaafc5416cd15c8759a273e660868

Observation 2ff16ee6-9d8e-4453-8a50-9c34888b4ef7 · outbound

This paper cites BERT: Pre- training of deep bidirectional transformers for language understanding,.

A Principled Framework for Multi-View Contrastive Learning BERT: Pre- training of deep bidirectional transformers for language understanding,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.565093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.420998Z digest=sha256:a3f7647ac31e1002b9d5427636b727070cd67952c0b58b96a1c84f0bf3170cf1

Observation a653cb5a-1244-48c4-89fc-2d66edc99b47 · outbound

This paper cites Hubert: Self-supervised speech representation learning by masked prediction of hidden units,.

A Principled Framework for Multi-View Contrastive Learning Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T19:02:14.424999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:02:14.424999Z digest=sha256:348993e353bed4b2684253692c8c7735a4fb26e294ac9bab54c94bf37f0a1a67

Observation 9d57fd5b-82c4-4d6c-b9a0-754386429ea7 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

A Principled Framework for Multi-View Contrastive Learning An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T19:02:14.428728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:02:14.428728Z digest=sha256:3dca08bdd7d9c371db04cb136423971c0165ad132c0017dd95de25a6f5a85afa

Observation bdc89af6-7217-4234-9fd0-8a6573349160 · outbound

This paper cites Decoupled weight decay regularization,.

A Principled Framework for Multi-View Contrastive Learning Decoupled weight decay regularization,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:02:14.539113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.432250Z digest=sha256:876a6726789b4b06235ef2b048cb43e9cd9b319d57ea8f981641f47a9b9baabc

Observation 77562faa-ffd6-45c3-ae8e-1fe1b7b59baa · outbound

This paper cites an unresolved cited work.

A Principled Framework for Multi-View Contrastive Learning Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:02:14.527096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:02:14.437243Z digest=sha256:bdece955073fe69caf8b1307ca27aa7c109104dcab8d172a3d1061ccf500ea25

Pith citing papers

No inbound Pith citation observations are available.