Paper Citation Record · LEDGER

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones

As of 20 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2506.05641.

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

pith.paper-citation-record.v1
2506.05641 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:20:23.528274Z

measured 44 of 44 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

44 of 44 outbound references displayed

verified exact4
verified fuzzy12
unresolved28
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6bee9b31-2d7a-411d-aea9-c1326c1eb397 · outbound

This paper cites write newline.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones write newline

Reference 1

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Observation 0d953fd4-9338-4c1a-abfe-bfa82963d17e · outbound

This paper cites Hyperstyle: Stylegan inversion with hypernetworks for real image editing.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Hyperstyle: Stylegan inversion with hypernetworks for real image editing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:24.212617Z

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Observation 916cede7-1b1b-463c-b869-797984d7d730 · outbound

This paper cites Generative pretraining from pixels.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Generative pretraining from pixels

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:24.196950Z

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Observation 715d2f6c-a967-4153-ac26-594df71f9257 · outbound

This paper cites an unresolved cited work.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Unresolved cited work

Reference 4

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Observation 4cc4256c-a7b8-4d46-9afe-4135f99a4ca6 · outbound

This paper cites Streamlining Redundant Layers to Compress Large Language Models.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Streamlining Redundant Layers to Compress Large Language Models

Reference 5

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Observation 275a067c-b886-4fd5-ae53-c8023bd7de5b · outbound

This paper cites Boosting Natural Language Generation from Instructions with Meta-Learning.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Boosting Natural Language Generation from Instructions with Meta-Learning

Reference 6

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Observation d699fe1f-2075-47aa-898e-b4be65cea802 · outbound

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

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Imagenet: A large-scale hierarchical image database

Reference 7

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Observation b893d2be-ada6-4e84-9d62-fa5cbdf9b7b3 · outbound

This paper cites M., Tran, A.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones M., Tran, A

Reference 8

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Observation 7a8ead0f-a2de-40b5-9a6a-d8562cdae8d8 · outbound

This paper cites HyperNetworks.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones HyperNetworks

Reference 9

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Observation 5d7068fc-e95b-4a07-a3c5-91f7637e81c9 · outbound

This paper cites Hyperprompt: Prompt-based task-conditioning of transformers.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Hyperprompt: Prompt-based task-conditioning of transformers

Reference 10

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Observation cf7bb954-6bd5-489c-9a54-29b05de8df58 · outbound

This paper cites Hyperdecoders: Instance-specific decoders for multi-task NLP.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Hyperdecoders: Instance-specific decoders for multi-task NLP

Reference 11

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Observation 43eac6e2-e438-441b-a128-46d665c518d1 · outbound

This paper cites HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation

Reference 12

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Observation 86b4b6db-6770-48ab-86ef-a2137cca3ae3 · outbound

This paper cites Scaling up gans for text-to-image synthesis.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Scaling up gans for text-to-image synthesis

Reference 13

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Observation de91b97f-d021-438a-984d-7936166ea8a4 · outbound

This paper cites W., and Romero Soriano, A.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones W., and Romero Soriano, A

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:24.133499Z

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Observation 21e1d121-5618-406f-ad35-ba98fc176130 · outbound

This paper cites MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning

Reference 15

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This paper cites Hart: Efficient adaptation via regularized autoregressive parameter generation.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Hart: Efficient adaptation via regularized autoregressive parameter generation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:24.118920Z

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Observation 7543a2fd-2b45-4c67-a734-077fe83da26d · outbound

This paper cites Weight Distillation: Transferring the Knowledge in Neural Network Parameters.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Weight Distillation: Transferring the Knowledge in Neural Network Parameters

Reference 17

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Observation 1995b6ab-1e12-460f-bfa4-9baa28950a70 · outbound

This paper cites and Wolf, L.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones and Wolf, L

Reference 18

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Observation 7be6e0e3-43ae-4cf7-91b1-9025df43c6bd · outbound

This paper cites LLM-Pruner: On the Structural Pruning of Large Language Models.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones LLM-Pruner: On the Structural Pruning of Large Language Models

Reference 19

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Observation 4e24ea5f-34e9-46ab-b714-0d79f5411bd6 · outbound

This paper cites Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks

Reference 20

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Learning to compress prompts with gist tokens

Reference 21

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Hyperseg: Patch-wise hypernetwork for real-time semantic segmentation

Reference 22

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Observation 18a11af9-940f-4006-aff5-4dc8e24ac5b9 · outbound

This paper cites Investigating the Effectiveness of HyperTuning via Gisting.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Investigating the Effectiveness of HyperTuning via Gisting

Reference 23

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Hypertuning: Toward adapting large language models without back-propagation

Reference 24

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This paper cites Language models are unsupervised multitask learners.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Language models are unsupervised multitask learners

Reference 25

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Unresolved cited work

Reference 26

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones High-resolution image synthesis with latent diffusion models

Reference 27

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Weight subcloning: direct initialization of transformers using larger pretrained ones

Reference 28

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Implicit neural representations with periodic activation functions

Reference 29

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones K., Tabor, J., Trzci \'n ski, T., et al

Reference 30

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Online Adaptation of Language Models with a Memory of Amortized Contexts

Reference 31

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones A Survey on Transformer Compression

Reference 32

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Hypergrid transformers: Towards a single model for multiple tasks

Reference 33

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Neural discrete representation learning

Reference 34

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Example-based hypernetworks for multi-source adaptation to unseen domains

Reference 35

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Continual learning with hypernetworks

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models

Reference 37

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Model Compression and Efficient Inference for Large Language Models: A Survey

Reference 38

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 39

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Initializing Models with Larger Ones

Reference 40

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Observation 5c73e39c-e67a-4057-8758-4ba43943c7b7 · outbound

This paper cites Learning to Generate Task-Specific Adapters from Task Description.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Learning to Generate Task-Specific Adapters from Task Description

Reference 41

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Graph HyperNetworks for Neural Architecture Search

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Observation da0e948b-b922-44f4-97a2-54290bd35183 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Adding conditional control to text-to-image diffusion models

Reference 43

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Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts

Reference 44

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Pith citing papers

No inbound Pith citation observations are available.