Paper Citation Record · LEDGER

Learning to Learn Weight Generation via Local Consistency Diffusion

As of 10 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2502.01117.

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

pith.paper-citation-record.v1
2502.01117 v3

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:40:50.239737Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T08:44:49.087457Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T08:45:19.141967Z

Reference resolution

55 of 55 outbound references displayed

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External citation measurements

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Outbound references

Observation a6019724-92df-4915-a20b-008669740595 · outbound

This paper cites Flow network based generative models for non-iterative diverse candidate generation.

Learning to Learn Weight Generation via Local Consistency Diffusion Flow network based generative models for non-iterative diverse candidate generation

Reference 1

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Observation 69e79792-3258-4ea3-8774-0340c6d88284 · outbound

This paper cites A closer look at the training strategy for modern meta-learning.

Learning to Learn Weight Generation via Local Consistency Diffusion A closer look at the training strategy for modern meta-learning

Reference 2

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Observation 1feb62f4-4270-4be1-b00b-d0cae0370b1f · outbound

This paper cites Generalization bounds for meta-learning: An information-theoretic analysis.

Learning to Learn Weight Generation via Local Consistency Diffusion Generalization bounds for meta-learning: An information-theoretic analysis

Reference 3

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Observation 0940ef2a-42aa-4f88-9d67-48c22afddc0f · outbound

This paper cites Meta-baseline: Exploring simple meta-learning for few-shot learning.

Learning to Learn Weight Generation via Local Consistency Diffusion Meta-baseline: Exploring simple meta-learning for few-shot learning

Reference 4

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Observation f2b0c864-2002-43ca-ad12-4955848f8028 · outbound

This paper cites Sophia Koepke, Ole Winther, and Zeynep Akata.

Learning to Learn Weight Generation via Local Consistency Diffusion Sophia Koepke, Ole Winther, and Zeynep Akata

Reference 5

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Observation 3e12d0c3-cece-486c-872f-732f166a0cdf · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning.

Learning to Learn Weight Generation via Local Consistency Diffusion An analysis of single-layer networks in unsupervised feature learning

Reference 6

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Observation 2a453617-e6dd-4af2-9e6c-1d9e1520eaa6 · outbound

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

Learning to Learn Weight Generation via Local Consistency Diffusion Imagenet: A large-scale hierarchical image database

Reference 7

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Observation f9c3e115-da01-43a8-a4b4-ac635a2bebec · outbound

This paper cites On stability and generalization of bilevel optimization problems.

Learning to Learn Weight Generation via Local Consistency Diffusion On stability and generalization of bilevel optimization problems

Reference 8

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Observation 780d35a6-4510-4d3d-85f1-15704d5e0151 · outbound

This paper cites Decaf: A deep convolutional activation feature for generic visual recognition.

Learning to Learn Weight Generation via Local Consistency Diffusion Decaf: A deep convolutional activation feature for generic visual recognition

Reference 9

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Learning to Learn Weight Generation via Local Consistency Diffusion Unresolved cited work

Reference 10

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Observation 1d8fa29e-9b56-4c8b-8fb2-69ed398d3fdc · outbound

This paper cites Ozdaglar.

Learning to Learn Weight Generation via Local Consistency Diffusion Ozdaglar

Reference 11

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Observation cf817afa-7957-46a2-9efc-0fbd64ad5319 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Learning to Learn Weight Generation via Local Consistency Diffusion Model-agnostic meta-learning for fast adaptation of deep networks

Reference 12

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Observation a094d387-82ba-4253-81cd-e6f763070477 · outbound

This paper cites Bootstrapped meta-learning.

Learning to Learn Weight Generation via Local Consistency Diffusion Bootstrapped meta-learning

Reference 13

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Observation ec53af9d-6683-4fcc-9bb9-7d9e582d69e9 · outbound

This paper cites Sharpness-aware mini- mization for efficiently improving generalization.

Learning to Learn Weight Generation via Local Consistency Diffusion Sharpness-aware mini- mization for efficiently improving generalization

Reference 14

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Observation 371ffa60-c577-4a60-8c2f-3224cc6fe10a · outbound

This paper cites An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications.

Learning to Learn Weight Generation via Local Consistency Diffusion An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications

Reference 15

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Observation 456b424e-e5c7-413d-9d1d-610aebc07e44 · outbound

This paper cites Task relatedness-based generalization bounds for meta learning.

Learning to Learn Weight Generation via Local Consistency Diffusion Task relatedness-based generalization bounds for meta learning

Reference 16

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Observation 88793c07-7d9b-4407-8f4d-a14f66d3cf1f · outbound

This paper cites Hierarchical meta-learning with hyper-tasks for few-shot learning.

Learning to Learn Weight Generation via Local Consistency Diffusion Hierarchical meta-learning with hyper-tasks for few-shot learning

Reference 17

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Learning to Learn Weight Generation via Local Consistency Diffusion Unresolved cited work

Reference 18

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This paper cites Deep residual learning for image recognition.

Learning to Learn Weight Generation via Local Consistency Diffusion Deep residual learning for image recognition

Reference 19

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This paper cites Denoising diffusion probabilistic models.

Learning to Learn Weight Generation via Local Consistency Diffusion Denoising diffusion probabilistic models

Reference 20

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This paper cites Meta-learning in neural networks: A survey.

Learning to Learn Weight Generation via Local Consistency Diffusion Meta-learning in neural networks: A survey

Reference 21

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This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen.

Learning to Learn Weight Generation via Local Consistency Diffusion Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen

Reference 22

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Learning to Learn Weight Generation via Local Consistency Diffusion Unresolved cited work

Reference 23

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This paper cites An information-theoretic analysis of the impact of task similarity on meta-learning.

Learning to Learn Weight Generation via Local Consistency Diffusion An information-theoretic analysis of the impact of task similarity on meta-learning

Reference 24

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This paper cites Information-theoretic generalization bounds for meta-learning and applications.

Learning to Learn Weight Generation via Local Consistency Diffusion Information-theoretic generalization bounds for meta-learning and applications

Reference 25

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This paper cites Adam: A method for stochastic optimization.

Learning to Learn Weight Generation via Local Consistency Diffusion Adam: A method for stochastic optimization

Reference 26

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This paper cites Kingma and Max Welling.

Learning to Learn Weight Generation via Local Consistency Diffusion Kingma and Max Welling

Reference 27

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This paper cites Taylor, and Adriana Romero-Soriano.

Learning to Learn Weight Generation via Local Consistency Diffusion Taylor, and Adriana Romero-Soriano

Reference 28

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Learning to Learn Weight Generation via Local Consistency Diffusion Unresolved cited work

Reference 29

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Observation 871fdc4d-efe9-41fc-b0ea-4311c36d8c8a · outbound

This paper cites Learning multiple layers of features from tiny images.

Learning to Learn Weight Generation via Local Consistency Diffusion Learning multiple layers of features from tiny images

Reference 30

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This paper cites Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B.

Learning to Learn Weight Generation via Local Consistency Diffusion Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B

Reference 31

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This paper cites Miller, and Mirek Riedewald.

Learning to Learn Weight Generation via Local Consistency Diffusion Miller, and Mirek Riedewald

Reference 32

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Learning to Learn Weight Generation via Local Consistency Diffusion Unresolved cited work

Reference 33

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This paper cites Understanding Diffusion Models: A Unified Perspective.

Learning to Learn Weight Generation via Local Consistency Diffusion Understanding Diffusion Models: A Unified Perspective

Reference 34

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Learning to Learn Weight Generation via Local Consistency Diffusion OCD: learning to overfit with conditional diffusion models

Reference 35

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Learning to Learn Weight Generation via Local Consistency Diffusion Fourier frequency tuning for parameter-efficient fine-tuning

Reference 36

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Learning to Learn Weight Generation via Local Consistency Diffusion Unresolved cited work

Reference 37

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Learning to Learn Weight Generation via Local Consistency Diffusion Fine-Grained Visual Classification of Aircraft

Reference 38

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Learning to Learn Weight Generation via Local Consistency Diffusion On First-Order Meta-Learning Algorithms

Reference 39

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Learning to Learn Weight Generation via Local Consistency Diffusion Hyperseg: Patch-wise hypernetwork for real-time semantic segmentation

Reference 40

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Learning to Learn Weight Generation via Local Consistency Diffusion Unresolved cited work

Reference 41

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Learning to Learn Weight Generation via Local Consistency Diffusion Cats and dogs

Reference 42

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This paper cites Early stopping-but when? In Neural Networks: Tricks of the trade , pages 55–69.

Learning to Learn Weight Generation via Local Consistency Diffusion Early stopping-but when? In Neural Networks: Tricks of the trade , pages 55–69

Reference 43

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Learning to Learn Weight Generation via Local Consistency Diffusion Rapid learning or feature reuse? towards understanding the effectiveness of MAML

Reference 44

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Learning to Learn Weight Generation via Local Consistency Diffusion Tenenbaum, Hugo Larochelle, and Richard S

Reference 45

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Learning to Learn Weight Generation via Local Consistency Diffusion Hyper- representations as generative models: Sampling unseen neural network weights

Reference 46

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Learning to Learn Weight Generation via Local Consistency Diffusion Diffusion-Based Neural Network Weights Generation

Reference 47

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Learning to Learn Weight Generation via Local Consistency Diffusion Meta- dataset: A dataset of datasets for learning to learn from few examples

Reference 48

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This paper cites Glue: A multi-task benchmark and analysis platform for natural language understanding.

Learning to Learn Weight Generation via Local Consistency Diffusion Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 49

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This paper cites Metadiff: Meta-learning with conditional diffusion for few-shot learning.

Learning to Learn Weight Generation via Local Consistency Diffusion Metadiff: Meta-learning with conditional diffusion for few-shot learning

Reference 50

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Learning to Learn Weight Generation via Local Consistency Diffusion Unresolved cited work

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Learning to Learn Weight Generation via Local Consistency Diffusion AdaLoRA: Towards efficient adaptive low-rank adaptation for large language models

Reference 52

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Learning to Learn Weight Generation via Local Consistency Diffusion Meta-learning via hypernetworks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:40:50.357080Z

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Observation 054a4c95-1580-4a40-ae8f-6b886f96120e · outbound

This paper cites DyLoRA: Parameter-efficient tuning of pre-trained models via dynamic low-rank adaptation.

Learning to Learn Weight Generation via Local Consistency Diffusion DyLoRA: Parameter-efficient tuning of pre-trained models via dynamic low-rank adaptation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:40:50.345088Z

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Observation f4c55e96-073c-474f-a30a-509820e5ea8d · outbound

This paper cites Unraveling model-agnostic meta-learning via the adaptation learning rate.

Learning to Learn Weight Generation via Local Consistency Diffusion Unraveling model-agnostic meta-learning via the adaptation learning rate

Reference 55

Resolution: malformed identifier; raw_fallback, observed 2026-08-09T16:40:50.332923Z

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Pith citing papers

Observation 13137625-d419-47f7-8a3e-c539c4ff0612 · inbound

RAM: Recover Any 3D Human Motion in-the-Wild cites this paper.

RAM: Recover Any 3D Human Motion in-the-Wild Learning to Learn Weight Generation via Local Consistency Diffusion

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-15T08:45:19.143587Z

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