Paper Citation Record · LEDGER

Sample- and Parameter-Efficient Auto-Regressive Image Models

As of 18 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2411.15648.

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

pith.paper-citation-record.v1
2411.15648 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: arxiv_reference, observed 2026-06-30T12:34:38.767663Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation 26a2ffc1-2480-4d1b-9488-e81b548e158a · outbound

This paper cites Self- supervised classification network.

Sample- and Parameter-Efficient Auto-Regressive Image Models Self- supervised classification network

Reference 1

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Observation 65723b29-d742-4f6f-836c-bb00d30435bd · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

Sample- and Parameter-Efficient Auto-Regressive Image Models Self-supervised learning from images with a joint-embedding predictive architecture

Reference 2

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Observation 3cec62c5-2775-4bcd-9ffa-a57acd0b67bd · outbound

This paper cites Data2vec: A general framework for self-supervised learning in speech, vision and language.

Sample- and Parameter-Efficient Auto-Regressive Image Models Data2vec: A general framework for self-supervised learning in speech, vision and language

Reference 3

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Observation d3f7037a-7a1c-4657-bc83-61c71b0d3c96 · outbound

This paper cites Efficient self-supervised learning with contextualized target representations for vision, speech and language.

Sample- and Parameter-Efficient Auto-Regressive Image Models Efficient self-supervised learning with contextualized target representations for vision, speech and language

Reference 4

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Observation e36d916d-ebdf-4942-b2d5-ee497c7a5e2d · outbound

This paper cites From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge.

Sample- and Parameter-Efficient Auto-Regressive Image Models From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge

Reference 5

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Observation 09bb5496-36dd-40d3-9cf7-6ee0e6449766 · outbound

This paper cites BEit: BERT pre-training of image transformers.

Sample- and Parameter-Efficient Auto-Regressive Image Models BEit: BERT pre-training of image transformers

Reference 6

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Observation 42e667fe-5c95-4a2d-b601-4895b1f85ee5 · outbound

This paper cites Stochastic positional embeddings improve masked image modeling.

Sample- and Parameter-Efficient Auto-Regressive Image Models Stochastic positional embeddings improve masked image modeling

Reference 7

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Observation baf8f8cd-c657-4798-8627-d36843070f6a · outbound

This paper cites The iWildCam 2021 Competition Dataset.

Sample- and Parameter-Efficient Auto-Regressive Image Models The iWildCam 2021 Competition Dataset

Reference 8

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Sample- and Parameter-Efficient Auto-Regressive Image Models Food- 101–mining discriminative components with random forests

Reference 9

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Observation 0f52cd52-53a1-4272-89bf-2a20f9f7a806 · outbound

This paper cites Language models are few-shot learners.

Sample- and Parameter-Efficient Auto-Regressive Image Models Language models are few-shot learners

Reference 10

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Observation 27c85b8a-9bdf-4eaa-aa2e-e9263d0e58be · outbound

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

Sample- and Parameter-Efficient Auto-Regressive Image Models Unsupervised learning of visual features by contrasting cluster assignments

Reference 11

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Observation 9b38cd3f-2c54-4d0e-bf49-f5a0b11141ee · outbound

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

Sample- and Parameter-Efficient Auto-Regressive Image Models Emerging properties in self-supervised vision transformers

Reference 12

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Observation b82da368-5353-48a6-8709-9d2ffc3bfe7d · outbound

This paper cites Generative pretraining from pixels.

Sample- and Parameter-Efficient Auto-Regressive Image Models Generative pretraining from pixels

Reference 13

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Sample- and Parameter-Efficient Auto-Regressive Image Models Unresolved cited work

Reference 14

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Observation dfa57423-894e-4481-8ba9-d554426edfbe · outbound

This paper cites Big self-supervised models are strong semi-supervised learners.

Sample- and Parameter-Efficient Auto-Regressive Image Models Big self-supervised models are strong semi-supervised learners

Reference 15

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Observation cc0a6d91-0027-4fe5-adb8-0bd682630898 · outbound

This paper cites Exploring simple siamese representation learning.

Sample- and Parameter-Efficient Auto-Regressive Image Models Exploring simple siamese representation learning

Reference 16

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Observation b300be9b-8b31-43d2-8cec-9b9065f1bcdf · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Sample- and Parameter-Efficient Auto-Regressive Image Models Improved Baselines with Momentum Contrastive Learning

Reference 17

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This paper cites An empirical study of training self-supervised vision transformers.

Sample- and Parameter-Efficient Auto-Regressive Image Models An empirical study of training self-supervised vision transformers

Reference 18

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Observation f83fd489-253b-4421-bec9-ad91e69c1d1e · outbound

This paper cites Context autoencoder for self-supervised representation learning.

Sample- and Parameter-Efficient Auto-Regressive Image Models Context autoencoder for self-supervised representation learning

Reference 19

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Observation edba8c04-d764-4ace-a2ea-d583a2d9bd57 · outbound

This paper cites Functional map of the world.

Sample- and Parameter-Efficient Auto-Regressive Image Models Functional map of the world

Reference 20

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This paper cites Describing textures in the wild.

Sample- and Parameter-Efficient Auto-Regressive Image Models Describing textures in the wild

Reference 21

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Observation 4df05ece-dcdc-4bb8-ac79-6fdd1afa53a1 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Sample- and Parameter-Efficient Auto-Regressive Image Models Imagenet: A large-scale hierarchical image database

Reference 22

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Observation c279809b-9127-4c5c-9c3a-b3fabc8d51e3 · outbound

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Sample- and Parameter-Efficient Auto-Regressive Image Models Discriminative unsupervised feature learning with convolutional neural networks

Reference 23

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Observation dee75b9b-a5ed-437b-99b7-6f0e4efc4012 · outbound

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

Sample- and Parameter-Efficient Auto-Regressive Image Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 24

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Observation 7b8b39a9-7069-410e-8cd8-2f9f264fdbd7 · outbound

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Sample- and Parameter-Efficient Auto-Regressive Image Models The Llama 3 Herd of Models

Reference 25

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This paper cites Scalable pre-training of large autoregressive image models.

Sample- and Parameter-Efficient Auto-Regressive Image Models Scalable pre-training of large autoregressive image models

Reference 26

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This paper cites Boot- strap your own latent - a new approach to self-supervised learning.

Sample- and Parameter-Efficient Auto-Regressive Image Models Boot- strap your own latent - a new approach to self-supervised learning

Reference 27

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Sample- and Parameter-Efficient Auto-Regressive Image Models Momentum contrast for unsupervised visual representation learning

Reference 28

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This paper cites Masked autoencoders are scalable vision learners.

Sample- and Parameter-Efficient Auto-Regressive Image Models Masked autoencoders are scalable vision learners

Reference 29

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Sample- and Parameter-Efficient Auto-Regressive Image Models Unresolved cited work

Reference 30

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This paper cites Scaling Laws for Neural Language Models.

Sample- and Parameter-Efficient Auto-Regressive Image Models Scaling Laws for Neural Language Models

Reference 31

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Sample- and Parameter-Efficient Auto-Regressive Image Models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 32

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Observation 092e0128-488e-45c6-939a-d581444330fa · outbound

This paper cites 3d object representations for fine-grained categorization.

Sample- and Parameter-Efficient Auto-Regressive Image Models 3d object representations for fine-grained categorization

Reference 33

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Observation d0f80a62-7dd9-4dda-9e22-fa9ae46b1c53 · outbound

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Sample- and Parameter-Efficient Auto-Regressive Image Models Learning multiple layers of features from tiny images

Reference 34

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Observation a66b0003-362d-4015-b682-14484624adc4 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Sample- and Parameter-Efficient Auto-Regressive Image Models DINOv2: Learning Robust Visual Features without Supervision

Reference 35

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Sample- and Parameter-Efficient Auto-Regressive Image Models Cats and dogs

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This paper cites Moment matching for multi-source do- main adaptation.

Sample- and Parameter-Efficient Auto-Regressive Image Models Moment matching for multi-source do- main adaptation

Reference 37

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This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Sample- and Parameter-Efficient Auto-Regressive Image Models Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 38

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Sample- and Parameter-Efficient Auto-Regressive Image Models Discrete Variational Autoencoders

Reference 39

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Sample- and Parameter-Efficient Auto-Regressive Image Models Spreading vectors for similarity search

Reference 40

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Sample- and Parameter-Efficient Auto-Regressive Image Models The effectiveness of mae pre-pretraining for billion-scale pretraining

Reference 41

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This paper cites Rxrx1: An image set for cellular morphological variation across many experimental batches.

Sample- and Parameter-Efficient Auto-Regressive Image Models Rxrx1: An image set for cellular morphological variation across many experimental batches

Reference 42

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Observation a5b4c883-2af4-4d4b-8718-533a9b7a1e3b · outbound

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Sample- and Parameter-Efficient Auto-Regressive Image Models Going deeper with image transformers

Reference 43

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Observation cd7bce01-dfcb-4a3c-92b2-6d92b49e9e3f · outbound

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Sample- and Parameter-Efficient Auto-Regressive Image Models LLaMA: Open and Efficient Foundation Language Models

Reference 44

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Sample- and Parameter-Efficient Auto-Regressive Image Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 45

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Sample- and Parameter-Efficient Auto-Regressive Image Models iNaturalist 2018 competition dataset

Reference 46

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Observation 25ba44aa-a88a-4350-b555-c85e086528a9 · outbound

This paper cites Rotation equivariant cnns for digital pathology.

Sample- and Parameter-Efficient Auto-Regressive Image Models Rotation equivariant cnns for digital pathology

Reference 47

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This paper cites Unsupervised feature learning via non-parametric instance dis- crimination.

Sample- and Parameter-Efficient Auto-Regressive Image Models Unsupervised feature learning via non-parametric instance dis- crimination

Reference 48

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Observation d5811c13-e2b9-49e4-a13b-d1f2dbad05ff · outbound

This paper cites Simmim: A simple framework for masked image modeling.

Sample- and Parameter-Efficient Auto-Regressive Image Models Simmim: A simple framework for masked image modeling

Reference 49

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Observation 80ada630-3e07-48d8-b035-3e4b8c3f465b · outbound

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Sample- and Parameter-Efficient Auto-Regressive Image Models iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 50

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Reference 51

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Observation 97dea497-a3d3-404a-b6b1-ad91599887b0 · outbound

This paper cites • Parameters (Linear): The number of parameters in the model determines the size of weight matrices involved in computation.

Sample- and Parameter-Efficient Auto-Regressive Image Models • Parameters (Linear): The number of parameters in the model determines the size of weight matrices involved in computation

Reference 52

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Pith citing papers

Observation 8cfcc209-2012-4853-bac9-ca5c93641d17 · inbound

Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation cites this paper.

Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation Sample- and Parameter-Efficient Auto-Regressive Image Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:34:38.769105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.