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Paper Citation Record · LEDGER

NAE: Normalizing AutoEncoder

As of 16 August 2026, this Paper Citation Record lists 100 of 295 outbound references and 0 inbound Pith citation observations for arXiv:2608.12084.

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

pith.paper-citation-record.v1
2608.12084 v1

Coverage vector

measured 100 of 295 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:22:07.681399Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

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Source: cited_works

Reference resolution

100 of 295 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved92
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch5

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e95a6b9a-17cc-49d9-82b0-1c641ccaabc7 · outbound

This paper cites an unresolved cited work.

NAE: Normalizing AutoEncoder Unresolved cited work

Reference 1

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Observation de5f47a7-eea7-466e-8016-9e3dd33db0dd · outbound

This paper cites A Survey of Complex-Valued Neural Networks.

NAE: Normalizing AutoEncoder A Survey of Complex-Valued Neural Networks

Reference 2

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Observation e3399ca0-97d1-41f7-ba2b-da196b28a08c · outbound

This paper cites an unresolved cited work.

NAE: Normalizing AutoEncoder Unresolved cited work

Reference 3

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Observation cb13eb2e-afa0-487e-8700-ca39ebbaefd4 · outbound

This paper cites Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency.

NAE: Normalizing AutoEncoder Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency

Reference 4

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Observation 832e4d17-8733-48ba-abe1-1370092d545c · outbound

This paper cites Adversarial Masking for Self-Supervised Learning.

NAE: Normalizing AutoEncoder Adversarial Masking for Self-Supervised Learning

Reference 5

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Observation c22d5ee6-72ee-402f-9d59-62821b619fae · outbound

This paper cites An Attention Free Transformer.

NAE: Normalizing AutoEncoder An Attention Free Transformer

Reference 6

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Observation e80baf6d-b31a-44c7-b200-a6a050df2ac1 · outbound

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NAE: Normalizing AutoEncoder Unresolved cited work

Reference 7

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Observation 89bede07-e8b2-4c62-8252-3ab3210c3429 · outbound

This paper cites Better plain ViT baselines for ImageNet-1k.

NAE: Normalizing AutoEncoder Better plain ViT baselines for ImageNet-1k

Reference 8

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Observation c321b4fb-97f7-4ad1-96a9-44d87aa3ab0a · outbound

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NAE: Normalizing AutoEncoder Unresolved cited work

Reference 9

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Observation ad02696a-20f6-4d1c-b82a-c5a7138f8d7a · outbound

This paper cites CNNs Avoid Curse of Dimensionality by Learning on Patches.

NAE: Normalizing AutoEncoder CNNs Avoid Curse of Dimensionality by Learning on Patches

Reference 10

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Observation f95b0388-a09d-4fd7-8669-6a31f40eb729 · outbound

This paper cites CoAtNet: Marrying Convolution and Attention for All Data Sizes.

NAE: Normalizing AutoEncoder CoAtNet: Marrying Convolution and Attention for All Data Sizes

Reference 11

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Observation fea9c5c5-67c6-48a8-a711-94271ac71974 · outbound

This paper cites ConvMAE: Masked Convolution Meets Masked Autoencoders.

NAE: Normalizing AutoEncoder ConvMAE: Masked Convolution Meets Masked Autoencoders

Reference 12

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Observation e097292c-ad4a-4674-9d9f-dd30111ae547 · outbound

This paper cites data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language.

NAE: Normalizing AutoEncoder data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

Reference 13

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Observation f9347a67-40c9-4d5d-81c1-2cac2a44db21 · outbound

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NAE: Normalizing AutoEncoder Unresolved cited work

Reference 14

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Observation 083153b5-b9da-4ef0-a998-2aceb2b1637a · outbound

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NAE: Normalizing AutoEncoder Unresolved cited work

Reference 15

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Observation 48245ccb-ce80-4e3e-a94c-047eff2f4c39 · outbound

This paper cites DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder.

NAE: Normalizing AutoEncoder DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder

Reference 16

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Observation f00026f8-75cc-4a4b-bb45-6a3ffb3d8610 · outbound

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NAE: Normalizing AutoEncoder Unresolved cited work

Reference 17

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Observation b9cce0e6-7a71-46fa-b473-aca06af98f69 · outbound

This paper cites Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction.

NAE: Normalizing AutoEncoder Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction

Reference 18

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Observation 7d40fa24-711f-4efb-b659-1841b60451da · outbound

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NAE: Normalizing AutoEncoder Efficient

Reference 19

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Observation ef4e8273-db4d-41ed-8620-c343a96dc717 · outbound

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NAE: Normalizing AutoEncoder EfficientFormer: Vision Transformers at MobileNet Speed

Reference 20

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Observation 5be3dc6b-0803-4824-90ca-54cee956ff0a · outbound

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NAE: Normalizing AutoEncoder Epistemic Neural Networks

Reference 21

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Observation efb86f50-f0c7-42d2-a2af-d0f84da01446 · outbound

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NAE: Normalizing AutoEncoder Fair Comparison between Efficient Attentions

Reference 22

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Observation 5ba3e4aa-10b7-44c0-84d6-0750a8b7966e · outbound

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NAE: Normalizing AutoEncoder Global Context Vision Transformers

Reference 23

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Observation 33266d0d-c6d2-4362-a54e-9ba1bb75d360 · outbound

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NAE: Normalizing AutoEncoder Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 24

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Observation a4ae21cb-a8c5-431a-836d-9e16fb8d578c · outbound

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NAE: Normalizing AutoEncoder How to Train Your

Reference 25

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Observation cc47cae7-b909-41ec-a57e-8ab0a1d1301a · outbound

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NAE: Normalizing AutoEncoder Improved Vector Quantized Diffusion Models

Reference 26

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Observation 47cb1655-e47c-48eb-b141-1c02e259e18f · outbound

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NAE: Normalizing AutoEncoder Improving Contrastive Learning with Model Augmentation

Reference 27

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Observation d7475b78-6e4c-479f-8594-d623188df8c4 · outbound

This paper cites No Reason for No Supervision: Improved Generalization in Supervised Models.

NAE: Normalizing AutoEncoder No Reason for No Supervision: Improved Generalization in Supervised Models

Reference 28

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Observation 73ce39bd-26d2-4c8f-add6-5c1228193f7d · outbound

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NAE: Normalizing AutoEncoder Information Geometry of Dropout Training

Reference 29

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Observation ba9e9012-743b-4179-8406-79232cc8807a · outbound

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NAE: Normalizing AutoEncoder LaMDA: Language Models for Dialog Applications

Reference 30

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Observation 565007b6-7a68-4824-b6dc-87feefde53e1 · outbound

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NAE: Normalizing AutoEncoder MaskViT: Masked Visual Pre-Training for Video Prediction

Reference 31

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Observation eaa26e45-5c23-4d2e-a206-9cac8d5c9913 · outbound

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NAE: Normalizing AutoEncoder Neural Collapse: A Review on Modelling Principles and Generalization

Reference 32

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Observation 104a2a09-2a06-48df-9c85-42749f3bd1c0 · outbound

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NAE: Normalizing AutoEncoder Neural Implicit Manifold Learning for Topology-Aware Density Estimation

Reference 33

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NAE: Normalizing AutoEncoder Neural Manifold Clustering and Embedding

Reference 34

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Observation bdff1898-8f68-44dd-8cda-82f419808660 · outbound

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NAE: Normalizing AutoEncoder On Neural Differential Equations

Reference 35

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Observation bb165fbf-2ecf-4b8a-9dd5-cff54bc088a0 · outbound

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NAE: Normalizing AutoEncoder On the Maximum Hessian Eigenvalue and Generalization

Reference 36

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Observation 772018ed-5b5b-439f-8370-6a8464832ca9 · outbound

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NAE: Normalizing AutoEncoder On the Measure of Intelligence

Reference 37

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Observation 2cce39e1-518e-4caa-b596-f2cc5743db36 · outbound

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NAE: Normalizing AutoEncoder Unresolved cited work

Reference 38

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Observation 0e66b12e-91dc-4400-bed5-121b6f8ddb1c · outbound

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NAE: Normalizing AutoEncoder Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 39

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Observation 0368450f-bcaa-40d2-bf81-f431815fc837 · outbound

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NAE: Normalizing AutoEncoder RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning

Reference 40

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Observation dc6e796f-0c51-40a7-8346-1f9ce19c50d7 · outbound

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NAE: Normalizing AutoEncoder Revitalizing

Reference 41

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Observation 4b8e3323-1463-4617-a701-2b063fce41a5 · outbound

This paper cites Robust and Efficient Medical Imaging with Self-Supervision.

NAE: Normalizing AutoEncoder Robust and Efficient Medical Imaging with Self-Supervision

Reference 42

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source=arxiv_source observed=2026-08-16T00:22:07.469366Z digest=sha256:b5a239ef137b848657640498affa37cc903aea3f96025cc65762a9c8596a4627

Observation c1599fb2-4196-447b-842f-b4e0cf52b873 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

NAE: Normalizing AutoEncoder Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 43

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source=arxiv_source observed=2026-08-16T00:22:07.472917Z digest=sha256:262a504b9b70b3e0b445de9e906925035231f2e1e4f00cd1cae39476dc4edd2b

Observation cca073a2-f13a-40a0-8650-f14772aa3c8f · outbound

This paper cites Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework.

NAE: Normalizing AutoEncoder Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework

Reference 44

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source=arxiv_source observed=2026-08-16T00:22:07.475878Z digest=sha256:4f4fb2384150ff09399026d26fe13328753039ccd59a260f402dfbc26a2b0a42

Observation 4713158c-4ae4-4fe6-9801-f722fd306f8a · outbound

This paper cites Self-Supervised.

NAE: Normalizing AutoEncoder Self-Supervised

Reference 45

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source=arxiv_source observed=2026-08-16T00:22:07.478954Z digest=sha256:51960d98c2e920b15a73028dc875c1b747559dc152275f45b815304ba6484704

Observation 008ca386-7a3f-4fa1-8fb6-2f3dc0c85a85 · outbound

This paper cites Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks.

NAE: Normalizing AutoEncoder Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks

Reference 46

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source=arxiv_source observed=2026-08-16T00:22:07.482232Z digest=sha256:465eb1ce008885464edd3805932299692942e76f8ef3965eddbde3c0cb936893

Observation 18301d1a-e6fc-4454-810f-66927207702d · outbound

This paper cites SiT: Self-supervised vIsion Transformer.

NAE: Normalizing AutoEncoder SiT: Self-supervised vIsion Transformer

Reference 47

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source=arxiv_source observed=2026-08-16T00:22:07.487328Z digest=sha256:6f1a268d8dbf2ee641f1035fdb157d219002bb90de686b78123e924c6c464a6d

Observation a666cacb-7557-4bc1-b39c-8b22eac796ff · outbound

This paper cites Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results.

NAE: Normalizing AutoEncoder Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results

Reference 48

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source=arxiv_source observed=2026-08-16T00:22:07.490995Z digest=sha256:5a4c74c2fc73cec2966c7999ed52f9695834f71e762b46daaa67e0469a374044

Observation 2bd53122-7499-4e72-8352-1c9d1089c6d6 · outbound

This paper cites Survey on Self-supervised Representation Learning Using Image Transformations.

NAE: Normalizing AutoEncoder Survey on Self-supervised Representation Learning Using Image Transformations

Reference 49

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source=arxiv_source observed=2026-08-16T00:22:07.494621Z digest=sha256:1d83160046e3f6aed26c3c6286c1667fb002d470e0330166f25055c72a33b948

Observation 5f8bddf1-f722-4f11-afe2-4948ed620294 · outbound

This paper cites Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

NAE: Normalizing AutoEncoder Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 50

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source=arxiv_source observed=2026-08-16T00:22:07.498354Z digest=sha256:37cbb2b44972a5c58a7bdae9752031a614a9f595f400bbac9ebc1216068cb5ca

Observation 39a90354-165f-46c2-8437-d1733b2f3707 · outbound

This paper cites The Uncanny Similarity of Recurrence and Depth.

NAE: Normalizing AutoEncoder The Uncanny Similarity of Recurrence and Depth

Reference 51

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source=arxiv_source observed=2026-08-16T00:22:07.501985Z digest=sha256:9774945484b9a13b2c82e6f18698eaf463357682e22c4ca3d34b76f6a814eb3c

Observation a7027d8a-28df-42d5-9108-fc7e745e7697 · outbound

This paper cites TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning.

NAE: Normalizing AutoEncoder TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning

Reference 52

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source=arxiv_source observed=2026-08-16T00:22:07.505578Z digest=sha256:55c6f456d1ad4431249946366d9af814fe0eeef700941bfd6d6a7a3054d070ef

Observation 6e9df63c-be00-445a-8a2b-823c27e339b8 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

NAE: Normalizing AutoEncoder Training data-efficient image transformers & distillation through attention

Reference 53

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source=arxiv_source observed=2026-08-16T00:22:07.509212Z digest=sha256:7f8c45c0560906d992d4c1faffda6d45918c760ce7a4fd89dfb82a27c9883340

Observation f27e22b1-635d-44b4-9ffb-37fa5870a60b · outbound

This paper cites TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition.

NAE: Normalizing AutoEncoder TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition

Reference 54

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source=arxiv_source observed=2026-08-16T00:22:07.512759Z digest=sha256:5223fc86889c694e08ee2f3d5b7fbddb2dd89995331fd83f5c8a617484a7c686

Observation 61747859-71ed-4b6b-95c9-2fae8fc5852f · outbound

This paper cites Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision.

NAE: Normalizing AutoEncoder Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision

Reference 55

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source=arxiv_source observed=2026-08-16T00:22:07.516838Z digest=sha256:cb975c189c47178c252745a6e5d37eb298027cc1a2a80bd259f18129d982a2aa

Observation a18ca1dc-bbe6-433b-9575-44aa22864757 · outbound

This paper cites Where are my Neighbors? Exploiting Patches Relations in Self-Supervised Vision Transformer.

NAE: Normalizing AutoEncoder Where are my Neighbors? Exploiting Patches Relations in Self-Supervised Vision Transformer

Reference 56

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source=arxiv_source observed=2026-08-16T00:22:07.520623Z digest=sha256:b577290c9299f1ca2252fa149ee02a474572aec52626c624527a46e0774400f8

Observation 1e6526b8-b485-47c5-8bb3-ef25a7c29729 · outbound

This paper cites X-ViT: High Performance Linear Vision Transformer without Softmax.

NAE: Normalizing AutoEncoder X-ViT: High Performance Linear Vision Transformer without Softmax

Reference 57

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source=arxiv_source observed=2026-08-16T00:22:07.524279Z digest=sha256:1d394b93b34697f8f02f5341dd95022faf3d5c230e5ef0f18b20adb60de42f90

Observation fdb59593-1e7d-4656-a9c5-00a9e0f28889 · outbound

This paper cites Zero-Shot Text-to-Image Generation.

NAE: Normalizing AutoEncoder Zero-Shot Text-to-Image Generation

Reference 58

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source=arxiv_source observed=2026-08-16T00:22:07.528089Z digest=sha256:72386a3c51348b8187ba3ff1e063f9bff5647438fbd00d7db1e34226a43379c4

Observation 67beb522-641a-4a2c-9ac1-197ecec13792 · outbound

This paper cites HindSight: A Graph-Based Vision Model Architecture For Representing Part-Whole Hierarchies.

NAE: Normalizing AutoEncoder HindSight: A Graph-Based Vision Model Architecture For Representing Part-Whole Hierarchies

Reference 59

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local_arxiv, observed 2026-08-16T00:23:41.702242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-16T00:22:07.531760Z digest=sha256:c21cc12da1d0b938f74a53e5baa7564a48f26f0df461822de09a14b45d995c36

Observation d7e3b65e-6ea1-489e-ba09-4015aa3b07d6 · outbound

This paper cites Paragraph-based Transformer Pre-training for Multi-Sentence Inference.

NAE: Normalizing AutoEncoder Paragraph-based Transformer Pre-training for Multi-Sentence Inference

Reference 60

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source=arxiv_source observed=2026-08-16T00:22:07.535352Z digest=sha256:da45ff155557b0e4bf19d5e286d6cb926cee97549a08edde7954a5a3559ce2f8

Observation cd2c6f56-e01d-4468-8511-16fd0107037d · outbound

This paper cites CLIPasso: Semantically-Aware Object Sketching.

NAE: Normalizing AutoEncoder CLIPasso: Semantically-Aware Object Sketching

Reference 61

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source=arxiv_source observed=2026-08-16T00:22:07.538953Z digest=sha256:4d554e6f8128e34e88f584c8eb27dcd16ac57e3dc7434b2174a201ac1ec5a0f6

Observation fac01ad9-16be-4396-b908-64b01b4ca6a6 · outbound

This paper cites Self-Supervised Visual Representation Learning from Hierarchical Grouping.

NAE: Normalizing AutoEncoder Self-Supervised Visual Representation Learning from Hierarchical Grouping

Reference 62

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source=arxiv_source observed=2026-08-16T00:22:07.542826Z digest=sha256:4b4e83170ff2e32bd2d6e7a1474ea2c508ad652a7be535b634cf705270ad3a01

Observation d9bdfc2d-8839-49d1-8c8f-387d61e265f7 · outbound

This paper cites What makes instance discrimination good for transfer learning?.

NAE: Normalizing AutoEncoder What makes instance discrimination good for transfer learning?

Reference 63

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source=arxiv_source observed=2026-08-16T00:22:07.546746Z digest=sha256:943cea943d0c963ddbfc9b5cf6daa48925bf61868c4060db3c73c4befb201956

Observation 4f7a65a3-566b-4817-93c8-c9ba079629f9 · outbound

This paper cites Early Convolutions Help Transformers See Better.

NAE: Normalizing AutoEncoder Early Convolutions Help Transformers See Better

Reference 64

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source=arxiv_source observed=2026-08-16T00:22:07.550583Z digest=sha256:7a1b065762ec91b1948b3d213afe17365e1aade6ac769985cc783d8d3044e253

Observation f033a130-16f1-418d-878e-54fb07a6f3fe · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

NAE: Normalizing AutoEncoder An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 65

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source=arxiv_source observed=2026-08-16T00:22:07.554504Z digest=sha256:c7c5472ec81730e2d0bcd8276273d9063ddf63cfadbf317fed7b04fe432b2260

Observation acb488cc-b72b-45b9-afab-2118d0c9d4f6 · outbound

This paper cites On the Relationship between Self-Attention and Convolutional Layers.

NAE: Normalizing AutoEncoder On the Relationship between Self-Attention and Convolutional Layers

Reference 66

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source=arxiv_source observed=2026-08-16T00:22:07.558331Z digest=sha256:aaea3bffde908b5a26760632a0cd61cef0fe01b8334d945e9b1b6a72bd98f46a

Observation 5d2672df-07b6-4552-b83d-555acc857037 · outbound

This paper cites International.

NAE: Normalizing AutoEncoder International

Reference 67

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source=arxiv_source observed=2026-08-16T00:22:07.561735Z digest=sha256:35b7e2b48603dab2a5b772b9f0ad9e9a89c51d52fcd3d4337f99f25141279772

Observation 78a04d3c-fad6-4cf1-8ca7-2662c3a9a3e5 · outbound

This paper cites Proceedings of the 37th.

NAE: Normalizing AutoEncoder Proceedings of the 37th

Reference 68

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source=arxiv_source observed=2026-08-16T00:22:07.564729Z digest=sha256:d65e4b79e2935647838c69016c7ba60b423e173ee1a3b573e3060c3476b215a4

Observation 63344016-8127-4f0f-a1d4-1d5559ccae53 · outbound

This paper cites Conditional Positional Encodings for Vision Transformers.

NAE: Normalizing AutoEncoder Conditional Positional Encodings for Vision Transformers

Reference 69

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source=arxiv_source observed=2026-08-16T00:22:07.568038Z digest=sha256:e9e7d8979a5b82fe5676f0a5d17c7ace591cb09fcbdf2051e60a62c81be179f2

Observation efa3882b-1e6a-4bd4-a302-4482f4112a55 · outbound

This paper cites Twins: Revisiting the Design of Spatial Attention in Vision Transformers.

NAE: Normalizing AutoEncoder Twins: Revisiting the Design of Spatial Attention in Vision Transformers

Reference 70

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source=arxiv_source observed=2026-08-16T00:22:07.571386Z digest=sha256:2e8624915b2580b64bd4566547c62bf9ae9d3060025ff68b464f50b38253b40b

Observation 137895e7-81ac-49ae-8f0d-e1c9ca5035a5 · outbound

This paper cites XCiT: Cross-Covariance Image Transformers.

NAE: Normalizing AutoEncoder XCiT: Cross-Covariance Image Transformers

Reference 71

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source=arxiv_source observed=2026-08-16T00:22:07.575429Z digest=sha256:ed4c315c24203388a24199743c3bf7e20997eeefacdf00aa60188fef10804e51

Observation 209f1b0f-a7ba-4614-aaac-36fb53d1cd00 · outbound

This paper cites Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition.

NAE: Normalizing AutoEncoder Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition

Reference 72

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source=arxiv_source observed=2026-08-16T00:22:07.579157Z digest=sha256:f95eec550c026672d99c7e4423bd8697b5bed2b4966e94ba183e6280a3855e08

Observation 7e3b444d-5f76-446e-8bf8-4195579436e5 · outbound

This paper cites ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias.

NAE: Normalizing AutoEncoder ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias

Reference 73

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local_arxiv, observed 2026-08-16T00:23:41.486977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-16T00:22:07.583072Z digest=sha256:362accd8909021b3e5f9455b862882153012fc66cc10d0c7d09d406637a2cda7

Observation bd7c8ee5-43e7-4633-97a2-74e543cb2b10 · outbound

This paper cites HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling.

NAE: Normalizing AutoEncoder HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling

Reference 74

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source=arxiv_source observed=2026-08-16T00:22:07.586896Z digest=sha256:fd18951407705ec2623276f516859ac798df4ad9899e593cb72bae4b30a7b71e

Observation 6f74c388-48f9-4938-834b-58c164f4f6ae · outbound

This paper cites QuadTree Attention for Vision Transformers.

NAE: Normalizing AutoEncoder QuadTree Attention for Vision Transformers

Reference 75

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source=arxiv_source observed=2026-08-16T00:22:07.590846Z digest=sha256:ed94441bb07d7ab0a3765e70a9a87a8cb866b17cfe347e980cfd3af6419cce88

Observation 97ebe45e-d919-4da9-b215-779e32a7c30a · outbound

This paper cites LocalViT: Analyzing Locality in Vision Transformers.

NAE: Normalizing AutoEncoder LocalViT: Analyzing Locality in Vision Transformers

Reference 76

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source=arxiv_source observed=2026-08-16T00:22:07.594500Z digest=sha256:a502e40f7b9a105521865de4bd026adf3c8be287e33f5a044786ed3ea4fb9b5e

Observation c253ba82-f804-484f-91f1-f7ebd378814c · outbound

This paper cites RegionViT: Regional-to-Local Attention for Vision Transformers.

NAE: Normalizing AutoEncoder RegionViT: Regional-to-Local Attention for Vision Transformers

Reference 77

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source=arxiv_source observed=2026-08-16T00:22:07.598422Z digest=sha256:dfc6eca148fd20950551fa7f6000045c40281e11a9fd00aa075c4cbc8cbcc887

Observation 5557f7ac-4b1d-4bc1-913c-97ee4edfd3e8 · outbound

This paper cites Selfie: Self-supervised Pretraining for Image Embedding.

NAE: Normalizing AutoEncoder Selfie: Self-supervised Pretraining for Image Embedding

Reference 78

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source=arxiv_source observed=2026-08-16T00:22:07.602569Z digest=sha256:89a3c8926844192158528b374d8e422a63648cf4ae687d49556f72dbb89df18a

Observation e8c9abb7-ee68-4d0d-8d79-28f10a162f87 · outbound

This paper cites PINs: Progressive Implicit Networks for Multi-Scale Neural Representations.

NAE: Normalizing AutoEncoder PINs: Progressive Implicit Networks for Multi-Scale Neural Representations

Reference 79

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source=arxiv_source observed=2026-08-16T00:22:07.606480Z digest=sha256:1f5bab2323b0f545d5809d6971433d320e952e42c33b6ec0bfa2de0a1fbbed12

Observation 270d4b63-0ff8-4601-a1b9-62ca0e71dc2a · outbound

This paper cites Masked Siamese ConvNets.

NAE: Normalizing AutoEncoder Masked Siamese ConvNets

Reference 80

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source=arxiv_source observed=2026-08-16T00:22:07.610349Z digest=sha256:bbe68bcfb3f14f57a34fa08af2eeb8324852af89434dac8764a4c7bc74d721ca

Observation 90aeeab0-5c39-4c1c-8cf6-6219506d0a33 · outbound

This paper cites Equivariant Contrastive Learning.

NAE: Normalizing AutoEncoder Equivariant Contrastive Learning

Reference 81

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source=arxiv_source observed=2026-08-16T00:22:07.614092Z digest=sha256:30dfe0b9f78e7de046edc313ad4570175bcd8f9c70df54c72f933081952395fc

Observation fab8a51f-d297-4dba-bb44-d2e291cc2001 · outbound

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

NAE: Normalizing AutoEncoder Understanding Dimensional Collapse in Contrastive Self-supervised Learning

Reference 82

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T00:22:07.617714Z digest=sha256:fcaeb748172b88eed2dfd759b051016b89667f55e8c4e719d7f26cd724a178c9

Observation 8131f68d-a91e-4918-9303-7f4913431e4f · outbound

This paper cites Barlow Twins: Self-Supervised Learning via Redundancy Reduction.

NAE: Normalizing AutoEncoder Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Reference 83

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source=arxiv_source observed=2026-08-16T00:22:07.621141Z digest=sha256:d13c1d53398bac0db3c46630b12f57c18fb3a29469c52d353c9d90a3a80a0ddb

Observation 7e36be43-e386-4aeb-b486-5a5db272fb61 · outbound

This paper cites Efficient Self-supervised Vision Transformers for Representation Learning.

NAE: Normalizing AutoEncoder Efficient Self-supervised Vision Transformers for Representation Learning

Reference 84

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no resolver link, observed 2026-08-16T00:22:07.624836Z

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source=arxiv_source observed=2026-08-16T00:22:07.624836Z digest=sha256:77af9ee002773c0ff1b043eb58cac44bb8626afc3be37752d43818c118275eb6

Observation 7661c542-6f20-441d-bc21-2e11e728a1a1 · outbound

This paper cites Visualizing and Understanding Contrastive Learning.

NAE: Normalizing AutoEncoder Visualizing and Understanding Contrastive Learning

Reference 85

Resolution
verified exact
local_arxiv, observed 2026-08-16T00:23:41.159185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-16T00:22:07.628255Z digest=sha256:f1e7aae4e4d9664928d457e09f84a769eba6e58c2262f1c38438130244e4ee28

Observation 8e2c660f-aa01-4e5a-bbfe-d7c80b509ec2 · outbound

This paper cites MST: Masked Self-Supervised Transformer for Visual Representation.

NAE: Normalizing AutoEncoder MST: Masked Self-Supervised Transformer for Visual Representation

Reference 86

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metadata mismatch
local_arxiv, observed 2026-08-16T00:23:41.135375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-16T00:22:07.631931Z digest=sha256:4628f2cc7ebb8ff44746bab01b60f09254350fc315142a48ac688095226579cc

Observation fab6a7b7-ff6f-4114-a7b4-e6266cebb093 · outbound

This paper cites Formal Algorithms for Transformers.

NAE: Normalizing AutoEncoder Formal Algorithms for Transformers

Reference 87

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no resolver link, observed 2026-08-16T00:22:07.635138Z

Source-reported events for the cited work

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Observation 5e664d3e-f47c-4f2f-8775-efce38083a81 · outbound

This paper cites Measuring Self-Supervised Representation Quality for Downstream Classification using Discriminative Features.

NAE: Normalizing AutoEncoder Measuring Self-Supervised Representation Quality for Downstream Classification using Discriminative Features

Reference 88

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verified exact
local_arxiv, observed 2026-08-16T00:23:41.017007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-16T00:22:07.638350Z digest=sha256:358e1456077f715eac10fd5cf4111e002c3651eabdad144a674b7ad3731f8a71

Observation c8bce2d2-ac28-45c3-8c53-f1b24e0ab10f · outbound

This paper cites PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers.

NAE: Normalizing AutoEncoder PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Reference 89

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no resolver link, observed 2026-08-16T00:22:07.641395Z

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source=arxiv_source observed=2026-08-16T00:22:07.641395Z digest=sha256:d4a013fe933352aeee1a9e2600d2bc5b961a81801ab4fcdc5d5835b46a035bca

Observation 04cb0b00-dcc3-4aa4-885b-bdcda099d3b3 · outbound

This paper cites Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?.

NAE: Normalizing AutoEncoder Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Reference 90

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no resolver link, observed 2026-08-16T00:22:07.644317Z

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source=arxiv_source observed=2026-08-16T00:22:07.644317Z digest=sha256:6d328b79e6260520c4e2846d925d7e9c900a1caaaed3df9fe8251cbfea30ce46

Observation 279a3187-b1e1-4cbc-9c25-12e13c63e65d · outbound

This paper cites Bootstrap your own latent: A new approach to self-supervised Learning.

NAE: Normalizing AutoEncoder Bootstrap your own latent: A new approach to self-supervised Learning

Reference 91

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no resolver link, observed 2026-08-16T00:22:07.647678Z

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source=arxiv_source observed=2026-08-16T00:22:07.647678Z digest=sha256:fb2a56850098a99930e616a60aa8f6013da6593c298d7404bd7c907afa7e7929

Observation ee490953-e825-4b59-9915-280e07a6bb0c · outbound

This paper cites What Makes for Good Views for Contrastive Learning?.

NAE: Normalizing AutoEncoder What Makes for Good Views for Contrastive Learning?

Reference 92

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no resolver link, observed 2026-08-16T00:22:07.651261Z

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source=arxiv_source observed=2026-08-16T00:22:07.651261Z digest=sha256:4b1732552e1555b87c2377f4fbb89590db85dfc5e94126e45c15ca86b9d461d9

Observation 988dd4cd-8fb7-4094-8ea8-649edf350ad2 · outbound

This paper cites MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning.

NAE: Normalizing AutoEncoder MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning

Reference 93

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no resolver link, observed 2026-08-16T00:22:07.654675Z

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source=arxiv_source observed=2026-08-16T00:22:07.654675Z digest=sha256:b894246f71b632e9807669e3817a4551fd80a8b90274fbb6ae121895a48a1fce

Observation 71771a7c-056e-4ada-9fba-fc72fc1bc580 · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

NAE: Normalizing AutoEncoder A Simple Framework for Contrastive Learning of Visual Representations

Reference 94

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source=arxiv_source observed=2026-08-16T00:22:07.658292Z digest=sha256:069c12a19f27014d13463680c7fb3e20032725448d6e8f74753abdfd7d133952

Observation 7e3806e3-198a-45c8-99e0-fa515c46861f · outbound

This paper cites ReZero is All You Need: Fast Convergence at Large Depth.

NAE: Normalizing AutoEncoder ReZero is All You Need: Fast Convergence at Large Depth

Reference 95

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no resolver link, observed 2026-08-16T00:22:07.662157Z

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source=arxiv_source observed=2026-08-16T00:22:07.662157Z digest=sha256:8ab16e13bc9781c382e91cd8dc61238b02068a2555a58c31fb6f7b1de0270401

Observation 5b003ca3-8a9d-4959-a4af-dbf1f482a312 · outbound

This paper cites Visual Parser: Representing Part-whole Hierarchies with Transformers.

NAE: Normalizing AutoEncoder Visual Parser: Representing Part-whole Hierarchies with Transformers

Reference 96

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source=arxiv_source observed=2026-08-16T00:22:07.666091Z digest=sha256:8efdc073e91e005aa35f1b10923f2af705febb5419f7da8de3aad580fe356c04

Observation ce496744-50d1-4760-8a39-4c4219f7c82e · outbound

This paper cites Peripheral Vision Transformer.

NAE: Normalizing AutoEncoder Peripheral Vision Transformer

Reference 97

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source=arxiv_source observed=2026-08-16T00:22:07.670240Z digest=sha256:ca64974848e43107c28b893da17872f74909d043fb031dff3f4de24a742bc266

Observation 0315a93f-1937-48f6-8b46-dfd423f6e8c5 · outbound

This paper cites Revisiting ResNets: Improved Training and Scaling Strategies.

NAE: Normalizing AutoEncoder Revisiting ResNets: Improved Training and Scaling Strategies

Reference 98

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metadata mismatch
local_arxiv, observed 2026-08-16T00:23:40.774361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-16T00:22:07.674024Z digest=sha256:85ea20bc48ac134a603d2d2ba1f64212068fb34c41afc6da708d56fe78b62f9c

Observation 98048345-c542-4b97-a455-35ee51be5f1d · outbound

This paper cites Self-Supervised.

NAE: Normalizing AutoEncoder Self-Supervised

Reference 99

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source=arxiv_source observed=2026-08-16T00:22:07.677887Z digest=sha256:766118280962177d888582ded2ada7effaefb56b9cea4bc599faafaadf4e3a04

Observation a85e7347-e64f-4221-9434-69f515a3e026 · outbound

This paper cites Understanding The Robustness in Vision Transformers.

NAE: Normalizing AutoEncoder Understanding The Robustness in Vision Transformers

Reference 100

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metadata mismatch
local_arxiv, observed 2026-08-16T00:23:40.697927Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-16T00:22:07.681399Z digest=sha256:0d16c5a10e8e84084a6a57d5659cabcdec4af57a9ab4c67c6dc3e08410fed88a

Pith citing papers

No inbound Pith citation observations are available.