Sparse Fine-Tuning of Transformers for Generative Tasks | Paper Citation Record

Observation 9511c46e-1fea-4a3b-adb3-c6a4f55fc39f · outbound

This paper cites Decomposing and interpreting image representations via text in vits beyond CLIP.

Sparse Fine-Tuning of Transformers for Generative Tasks Decomposing and interpreting image representations via text in vits beyond CLIP

Reference 1

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This paper cites A fast iterative shrinkage- thresholding algorithm for linear inverse problems.

Sparse Fine-Tuning of Transformers for Generative Tasks A fast iterative shrinkage- thresholding algorithm for linear inverse problems

Reference 2

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This paper cites Longformer: The Long-Document Transformer.

Sparse Fine-Tuning of Transformers for Generative Tasks Longformer: The Long-Document Transformer

Reference 3

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This paper cites Rep- resentation learning: A review and new perspectives.

Sparse Fine-Tuning of Transformers for Generative Tasks Rep- resentation learning: A review and new perspectives

Reference 4

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This paper cites Towards monosemanticity: De- composing language models with dictionary learning.

Sparse Fine-Tuning of Transformers for Generative Tasks Towards monosemanticity: De- composing language models with dictionary learning

Reference 5

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Observation 5c1f3671-1906-4e2f-9f36-d72a314c457e · outbound

This paper cites Compressive sampling.

Sparse Fine-Tuning of Transformers for Generative Tasks Compressive sampling

Reference 6

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This paper cites Pixart- σ: Weak-to-strong training of dif- fusion transformer for 4k text-to-image generation.

Sparse Fine-Tuning of Transformers for Generative Tasks Pixart- σ: Weak-to-strong training of dif- fusion transformer for 4k text-to-image generation

Reference 7

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Observation 096e96c4-dabc-4ba2-8170-74f55abbc1b2 · outbound

This paper cites Large convolutional model tuning via filter subspace.

Sparse Fine-Tuning of Transformers for Generative Tasks Large convolutional model tuning via filter subspace

Reference 8

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Observation d69b7fec-a966-4382-8b22-dfebd119467e · outbound

This paper cites Graph Convolution with Low-rank Learnable Local Filters.

Sparse Fine-Tuning of Transformers for Generative Tasks Graph Convolution with Low-rank Learnable Local Filters

Reference 9

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Observation 3ce6576a-d060-42a4-9d2f-853414c06544 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Sparse Fine-Tuning of Transformers for Generative Tasks Generating Long Sequences with Sparse Transformers

Reference 10

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Observation e8b053f3-b16f-4d1f-85f3-65e9c598153d · outbound

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

Sparse Fine-Tuning of Transformers for Generative Tasks An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 11

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Observation e733b948-10e0-42db-a9fa-084f9ae63278 · outbound

This paper cites Transcoders enable fine-grained interpretable circuit analy- sis for language models.

Sparse Fine-Tuning of Transformers for Generative Tasks Transcoders enable fine-grained interpretable circuit analy- sis for language models

Reference 12

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Observation 7f56e7ad-4ed9-49fd-879a-daa117a23228 · outbound

This paper cites The Vendi Score: A Diversity Evaluation Metric for Machine Learning.

Sparse Fine-Tuning of Transformers for Generative Tasks The Vendi Score: A Diversity Evaluation Metric for Machine Learning

Reference 13

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This paper cites Svdiff: Compact param- eter space for diffusion fine-tuning.

Sparse Fine-Tuning of Transformers for Generative Tasks Svdiff: Compact param- eter space for diffusion fine-tuning

Reference 14

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Observation 7de6e6f4-2ea0-4c63-b085-3349a69f266c · outbound

This paper cites Conceptexpress: Harnessing diffusion models for single-image unsupervised concept extraction.

Sparse Fine-Tuning of Transformers for Generative Tasks Conceptexpress: Harnessing diffusion models for single-image unsupervised concept extraction

Reference 15

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This paper cites Lora: Low- rank adaptation of large language models.

Sparse Fine-Tuning of Transformers for Generative Tasks Lora: Low- rank adaptation of large language models

Reference 16

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Observation 6243c206-58f7-4441-873e-e681dc3c66d9 · outbound

This paper cites SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation.

Sparse Fine-Tuning of Transformers for Generative Tasks SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation

Reference 17

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Observation d56e6243-efed-47b1-91b9-b4f6a5e88808 · outbound

This paper cites Text embed- ding is not all you need: Attention control for text-to-image semantic alignment with text self-attention maps.

Sparse Fine-Tuning of Transformers for Generative Tasks Text embed- ding is not all you need: Attention control for text-to-image semantic alignment with text self-attention maps

Reference 18

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Observation 30ca9e2b-c1cb-4e00-8ac2-9606dd80a881 · outbound

This paper cites Learning to Customize Text-to-Image Diffusion In Diverse Context.

Sparse Fine-Tuning of Transformers for Generative Tasks Learning to Customize Text-to-Image Diffusion In Diverse Context

Reference 19

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Observation 3835d217-b7bd-41d3-8148-244890f980a7 · outbound

This paper cites An introduction to variational autoencoders.

Sparse Fine-Tuning of Transformers for Generative Tasks An introduction to variational autoencoders

Reference 20

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This paper cites Segment any- thing.

Sparse Fine-Tuning of Transformers for Generative Tasks Segment any- thing

Reference 21

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This paper cites Multi-concept customization of text-to-image diffusion.

Sparse Fine-Tuning of Transformers for Generative Tasks Multi-concept customization of text-to-image diffusion

Reference 22

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Observation da99d513-7753-4c4a-9aec-2bf4bf27ee7d · outbound

This paper cites FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference.

Sparse Fine-Tuning of Transformers for Generative Tasks FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference

Reference 23

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Observation a0c2c29f-1e56-4d25-85f1-c33a7507450e · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

Sparse Fine-Tuning of Transformers for Generative Tasks DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 24

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This paper cites Re-Imagining Multimodal Instruction Tuning: A Representation View.

Sparse Fine-Tuning of Transformers for Generative Tasks Re-Imagining Multimodal Instruction Tuning: A Representation View

Reference 25

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Observation 73856219-331c-496e-bcdb-fb41f05e0c1f · outbound

This paper cites CAME: Confidence-guided Adaptive Memory Efficient Optimization.

Sparse Fine-Tuning of Transformers for Generative Tasks CAME: Confidence-guided Adaptive Memory Efficient Optimization

Reference 26

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Observation d70aca7b-cc9b-47dd-a216-6d314df84f84 · outbound

This paper cites Supervised dictionary learning.

Sparse Fine-Tuning of Transformers for Generative Tasks Supervised dictionary learning

Reference 27

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Observation bb28413e-c0a7-476d-a41b-ae3380648e15 · outbound

This paper cites Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.

Sparse Fine-Tuning of Transformers for Generative Tasks Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 28

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Observation 9e79c806-f05c-446c-90c1-3fbdd0f7001d · outbound

This paper cites Con- tinual learning with filter atom swapping.

Sparse Fine-Tuning of Transformers for Generative Tasks Con- tinual learning with filter atom swapping

Reference 29

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Observation d9a52c6a-4468-409f-8888-94d639654a8a · outbound

This paper cites Spatiotemporal joint filter decomposition in 3d convolutional neural networks.

Sparse Fine-Tuning of Transformers for Generative Tasks Spatiotemporal joint filter decomposition in 3d convolutional neural networks

Reference 30

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Observation ad1d9996-b7bf-4c0c-9e6f-f041426bb985 · outbound

This paper cites Training diffusion models towards diverse image generation with reinforcement learning.

Sparse Fine-Tuning of Transformers for Generative Tasks Training diffusion models towards diverse image generation with reinforcement learning

Reference 31

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Observation a2cac26c-8e79-4f7d-9e21-ecd4ccb78517 · outbound

This paper cites Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization.

Sparse Fine-Tuning of Transformers for Generative Tasks Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization

Reference 32

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This paper cites Coeff-tuning: A graph filter subspace view for tuning attention-based large models.

Sparse Fine-Tuning of Transformers for Generative Tasks Coeff-tuning: A graph filter subspace view for tuning attention-based large models

Reference 33

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Observation 4226990e-0c4e-420d-bfd4-4b963543249b · outbound

This paper cites Emergence of simple- cell receptive field properties by learning a sparse code for natural images.

Sparse Fine-Tuning of Transformers for Generative Tasks Emergence of simple- cell receptive field properties by learning a sparse code for natural images

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:15.683911Z

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Observation 00d3af64-ebc0-49a5-9126-3751ca30ec2f · outbound

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

Sparse Fine-Tuning of Transformers for Generative Tasks DINOv2: Learning Robust Visual Features without Supervision

Reference 35

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Observation 129da327-983e-4cae-894e-57db7ff3ddf4 · outbound

This paper cites Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement.

Sparse Fine-Tuning of Transformers for Generative Tasks Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement

Reference 36

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Observation 82d6a9a0-8b77-4e86-b3d9-e90d4894b5a9 · outbound

This paper cites Scalable diffusion models with transformers.

Sparse Fine-Tuning of Transformers for Generative Tasks Scalable diffusion models with transformers

Reference 37

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Observation 08d02a7f-6875-4edd-a70e-3a8a6c5bbcd4 · outbound

This paper cites Posterior sampling via Langevin dynamics based on generative priors.

Sparse Fine-Tuning of Transformers for Generative Tasks Posterior sampling via Langevin dynamics based on generative priors

Reference 38

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Observation 9d05a825-ac72-4027-9e9a-4b07fed1450b · outbound

This paper cites Dcfnet: Deep neural network with decomposed convolutional filters.

Sparse Fine-Tuning of Transformers for Generative Tasks Dcfnet: Deep neural network with decomposed convolutional filters

Reference 39

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Observation 0dbe1858-2f38-4fc6-adc9-797e0d1c4f37 · outbound

This paper cites Controlling text-to-image diffusion by orthogo- nal finetuning.

Sparse Fine-Tuning of Transformers for Generative Tasks Controlling text-to-image diffusion by orthogo- nal finetuning

Reference 40

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Observation ace7e34a-7272-4691-b644-997b24177ea2 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Sparse Fine-Tuning of Transformers for Generative Tasks Learning transferable visual models from natural language supervi- sion

Reference 41

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Observation 876665a2-20a8-42e7-8e31-45eaa8af2c1d · outbound

This paper cites Unveiling and mitigating mem- orization in text-to-image diffusion models through cross at- tention.

Sparse Fine-Tuning of Transformers for Generative Tasks Unveiling and mitigating mem- orization in text-to-image diffusion models through cross at- tention

Reference 42

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Observation b318066c-1caa-4b6d-89c2-7b4135f8deb3 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

Sparse Fine-Tuning of Transformers for Generative Tasks High-resolution image syn- thesis with latent diffusion models

Reference 43

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Observation eae68459-335e-42c9-9f79-2dd68d48081f · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Sparse Fine-Tuning of Transformers for Generative Tasks Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 44

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Observation f54b1b02-f063-4457-903f-236c4c0922c6 · outbound

This paper cites Unpacking sdxl turbo: Interpreting text-to-image models with sparse au- toencoders.

Sparse Fine-Tuning of Transformers for Generative Tasks Unpacking sdxl turbo: Interpreting text-to-image models with sparse au- toencoders

Reference 45

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Observation 29c649b9-94a5-4f6a-a97d-e665b6fa74cc · outbound

This paper cites Sparse sinkhorn attention.

Sparse Fine-Tuning of Transformers for Generative Tasks Sparse sinkhorn attention

Reference 46

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Observation 68793c4a-d8bc-43c8-b927-4c223eb6b933 · outbound

This paper cites Longer Attention Span: Increasing Transformer Context Length with Sparse Graph Processing Techniques.

Sparse Fine-Tuning of Transformers for Generative Tasks Longer Attention Span: Increasing Transformer Context Length with Sparse Graph Processing Techniques

Reference 47

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Observation dad8f7eb-841b-43dd-87e0-f4984279b962 · outbound

This paper cites Attention is all you need.

Sparse Fine-Tuning of Transformers for Generative Tasks Attention is all you need

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:15.482518Z

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Observation dbcc7baf-124c-4b95-9dd1-785ae1449fe4 · outbound

This paper cites Stochastic Conditional Generative Networks with Basis Decomposition.

Sparse Fine-Tuning of Transformers for Generative Tasks Stochastic Conditional Generative Networks with Basis Decomposition

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:30:15.040899Z

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Observation 8879c03b-fe14-48c9-8b58-fae899a69f9f · outbound

This paper cites Image generation using continuous filter atoms.

Sparse Fine-Tuning of Transformers for Generative Tasks Image generation using continuous filter atoms

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:15.470459Z

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Observation 1cde644b-e6fb-42af-8dd7-e72b3aa0e45b · outbound

This paper cites Adaptive convolutions with per-pixel dynamic filter atom.

Sparse Fine-Tuning of Transformers for Generative Tasks Adaptive convolutions with per-pixel dynamic filter atom

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:15.458829Z

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Observation 5148cfb5-adff-4abc-963d-589070319bdd · outbound

This paper cites Advancing parameter efficiency in fine-tuning via representation editing.

Sparse Fine-Tuning of Transformers for Generative Tasks Advancing parameter efficiency in fine-tuning via representation editing

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:15.446692Z

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Observation 8b95fe18-a91b-4d50-abd3-f9d3c7096558 · outbound

This paper cites Reft: Representation finetuning for language models.

Sparse Fine-Tuning of Transformers for Generative Tasks Reft: Representation finetuning for language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:15.435755Z

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Observation bba9694a-4656-4932-b54f-0b645d47c6db · outbound

This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.

Sparse Fine-Tuning of Transformers for Generative Tasks Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 54

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Observation 316a5f64-7c05-4e57-bef7-91016736f517 · outbound

This paper cites an unresolved cited work.

Sparse Fine-Tuning of Transformers for Generative Tasks Unresolved cited work

Reference 55

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Source-reported events for the cited work

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Observation 8bafb27a-d84f-488f-86c2-879c47e3b7a2 · outbound

This paper cites Enhancing semantic fidelity in text-to-image synthesis: Attention regulation in diffusion models.

Sparse Fine-Tuning of Transformers for Generative Tasks Enhancing semantic fidelity in text-to-image synthesis: Attention regulation in diffusion models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:15.404694Z

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Observation bd515564-5b75-46c1-9d9f-cf33c271640c · outbound

This paper cites Object- conditioned energy-based attention map alignment in text-to- image diffusion models.

Sparse Fine-Tuning of Transformers for Generative Tasks Object- conditioned energy-based attention map alignment in text-to- image diffusion models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:15.391151Z

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Observation a9bd85a6-a7e8-4dd8-88cf-71992dbb7594 · outbound

This paper cites A grey ⟨V ⟩ wolf plushie.

Sparse Fine-Tuning of Transformers for Generative Tasks A grey ⟨V ⟩ wolf plushie

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:15.378776Z

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

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