Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation | Paper Citation Record

Observation 4b763389-f975-44c6-8757-f3031ccd2729 · outbound

This paper cites Bit- Fit: Simple parameter-efficient fine-tuning for transformer- based masked language-models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Bit- Fit: Simple parameter-efficient fine-tuning for transformer- based masked language-models

Reference 1

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Observation bd99f8a7-c227-4843-9c33-82c1ffb2fce2 · outbound

This paper cites Food-101–mining discriminative components with random forests.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Food-101–mining discriminative components with random forests

Reference 2

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Observation 59359242-61ba-4fe8-8d6f-54314ac4521a · outbound

This paper cites LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery

Reference 3

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This paper cites Fira: Can we achieve full-rank training of llms under low-rank constraint? arXiv preprint arXiv:2410.01623, 2024.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Fira: Can we achieve full-rank training of llms under low-rank constraint? arXiv preprint arXiv:2410.01623, 2024

Reference 4

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This paper cites A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations

Reference 5

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Observation 024fa9bf-68bf-4ba3-a8c9-5d6ee8392e0a · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Reproducible scaling laws for contrastive language-image learning

Reference 6

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Observation ee4bc03d-5c2c-45b7-a486-a80c76873321 · outbound

This paper cites Describing textures in the wild.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Describing textures in the wild

Reference 7

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Observation 8f32c208-df55-464b-a928-c3ac9ccf7b83 · outbound

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

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Imagenet: A large-scale hierarchical image database

Reference 8

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Observation e70a8c91-d79f-4b0f-ab7f-6492350c5889 · outbound

This paper cites Everybody prune now: Structured pruning of llms with only forward passes.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Everybody prune now: Structured pruning of llms with only forward passes

Reference 9

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Observation c49d0108-3498-47d2-be2d-2cc91021d66f · outbound

This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 10

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Observation 4bd8a2f4-3541-4800-aebe-637c6a775b81 · outbound

This paper cites ROSA: Random Subspace Adaptation for Efficient Fine-Tuning.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation ROSA: Random Subspace Adaptation for Efficient Fine-Tuning

Reference 11

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Observation 65d56f6e-e081-4ef5-ba98-9b628573cd61 · outbound

This paper cites SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining

Reference 12

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Observation 20343172-f602-468d-bf80-80b6e02fd08c · outbound

This paper cites Towards a unified view of parameter-efficient transfer learning.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Towards a unified view of parameter-efficient transfer learning

Reference 13

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Observation db4f19a6-af90-4d86-80f5-e470e59c8966 · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 14

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Observation cbebcd20-0c56-4d5d-85f1-fc1bae8af2b8 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Parameter-efficient transfer learning for nlp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.232404Z

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Observation 254ab4a0-b492-4b18-9b1f-606fcc9dee9e · outbound

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

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation LoRA: Low-rank adaptation of large language models

Reference 16

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Observation 124434b1-1723-422d-a5ac-ade9defec8ca · outbound

This paper cites Do sparse brain activ- ity patterns underlie human cognition? NeuroImage, 263: 119633, 2022.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Do sparse brain activ- ity patterns underlie human cognition? NeuroImage, 263: 119633, 2022

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.211762Z

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Observation db920feb-20a1-4215-b6f9-e4909b263505 · outbound

This paper cites Vi- sual prompt tuning.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Vi- sual prompt tuning

Reference 18

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Observation 3cf784bd-9007-40f5-9658-add475d9f722 · outbound

This paper cites Adam: A method for stochastic optimization.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Adam: A method for stochastic optimization

Reference 19

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Observation fe107bfb-b69a-4c16-a9b8-686d7da904cf · outbound

This paper cites VeRA: Vector-based random matrix adaptation.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation VeRA: Vector-based random matrix adaptation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.175380Z

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Observation 58639b98-16ae-493c-bf20-bf1783d5b2a3 · outbound

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

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation 3d object representations for fine-grained categorization

Reference 21

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Observation 6ec408eb-8f18-4c57-ae86-a6daae0e79ab · outbound

This paper cites Block pruning for faster transformers.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Block pruning for faster transformers

Reference 22

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Observation f0b70be3-87a7-4fbc-8f8b-7bb51ed153ef · outbound

This paper cites Relora: High-rank training through low-rank updates.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Relora: High-rank training through low-rank updates

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.139953Z

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Observation a16c9667-beb4-4b56-acdd-77c1e4bf909c · outbound

This paper cites NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models

Reference 24

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Observation 98547500-9f5c-4ae7-a6ed-056bb61f0f34 · outbound

This paper cites DoRA: Weight-decomposed low-rank adaptation.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation DoRA: Weight-decomposed low-rank adaptation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.127072Z

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Observation 021329c7-ae0d-432f-9d3b-27104159e046 · outbound

This paper cites Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.114649Z

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Observation d0513266-895b-4120-90b2-584a77f72d3a · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Llm-pruner: On the structural pruning of large language models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.102357Z

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Observation e8d7a183-8440-4091-a942-c728b42a1db1 · outbound

This paper cites Fine-grained visual classi- fication of aircraft.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Fine-grained visual classi- fication of aircraft

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.090748Z

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Observation c0f5efc4-0232-4868-a634-9f64cb6fde1b · outbound

This paper cites PiSSA: Principal singular values and singular vectors adaptation of large language models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation PiSSA: Principal singular values and singular vectors adaptation of large language models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.075979Z

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Observation 1085c22d-531f-43be-bba3-bb6d3c7e075b · outbound

This paper cites Automated flower classification over a large number of classes.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Automated flower classification over a large number of classes

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.063191Z

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Observation 7679b86b-69b3-4126-ab53-e8a420b5b443 · outbound

This paper cites Lisa: Layerwise importance sampling for memory-efficient large language model fine- tuning.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Lisa: Layerwise importance sampling for memory-efficient large language model fine- tuning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.049969Z

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Observation 6952c1ab-4925-41ab-a765-c488357d75c2 · outbound

This paper cites Cats and dogs.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Cats and dogs

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.036771Z

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Observation bf0f5305-ca05-4d3d-9ad0-701b93d45fe0 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Learn- ing transferable visual models from natural language super- vision

Reference 33

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Observation 4eaedcae-dba4-42a9-970b-59eeb5a9e4e6 · outbound

This paper cites A closer look at the few-shot adaptation of large vision-language models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation A closer look at the few-shot adaptation of large vision-language models

Reference 34

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Observation 9e56f9a5-52c0-41e5-864b-e2ddfe81452d · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 35

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Observation d17d9ea9-e4ab-4d9b-b95f-a0e2f86b4891 · outbound

This paper cites A simple and effective pruning approach for large language models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation A simple and effective pruning approach for large language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:17.002210Z

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Observation 0222cb59-b50c-4292-a6d8-faa50af7e058 · outbound

This paper cites A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model

Reference 37

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Observation 1d763693-e3a0-402a-b255-23fc1274303a · outbound

This paper cites Training neu- ral networks with fixed sparse masks.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Training neu- ral networks with fixed sparse masks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.990866Z

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Observation bce9bfe2-e0ab-4ff7-9f24-950f3d455c6e · outbound

This paper cites Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.976893Z

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Observation 7970666b-7ae8-44fe-8191-5c094675269d · outbound

This paper cites A simple model for behav- ioral time scale synaptic plasticity (btsp) provides content ad- dressable memory with binary synapses and one-shot learn- ing.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation A simple model for behav- ioral time scale synaptic plasticity (btsp) provides content ad- dressable memory with binary synapses and one-shot learn- ing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.965402Z

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Observation 5b67504d-ee2a-43ee-9a3e-70f0ed0bc60a · outbound

This paper cites Struc- tured pruning learns compact and accurate models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Struc- tured pruning learns compact and accurate models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.953762Z

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Observation 94611f9d-f0a6-4c51-ad3e-12cb9f312c06 · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Sun database: Large-scale scene recognition from abbey to zoo

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.941625Z

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Observation 16e4f042-4e3e-4587-bce8-0bd5ad2b487a · outbound

This paper cites Raise a child in large language model: Towards effective and generalizable fine-tuning.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Raise a child in large language model: Towards effective and generalizable fine-tuning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.929184Z

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Observation ad64e988-6f9a-43a5-8bb4-8913b65781da · outbound

This paper cites CorDA: Context-oriented decomposition adaptation of large language models for task-aware parameter-efficient fine- tuning.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation CorDA: Context-oriented decomposition adaptation of large language models for task-aware parameter-efficient fine- tuning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.916808Z

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Observation 33c08aae-7bf8-4317-816a-49470c97a9fc · outbound

This paper cites Task residual for tuning vision-language models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Task residual for tuning vision-language models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.904018Z

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Observation cf42375a-3359-4a78-b1de-a9883afef9a2 · outbound

This paper cites Low-rank few-shot adaptation of vision-language models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Low-rank few-shot adaptation of vision-language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.892254Z

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Observation 2eebf403-26bc-4bd0-891e-2a536312136c · outbound

This paper cites Gradient- based parameter selection for efficient fine-tuning.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Gradient- based parameter selection for efficient fine-tuning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.880405Z

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Observation 6aa21d53-3e0c-4aa1-9aac-712dae383aa8 · outbound

This paper cites Galore: Memory- efficient llm training by gradient low-rank projection.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Galore: Memory- efficient llm training by gradient low-rank projection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.868378Z

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Observation 1453bc19-85aa-4904-8c60-ca166b78cff6 · outbound

This paper cites Conditional prompt learning for vision-language models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Conditional prompt learning for vision-language models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.853724Z

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Observation 103429c2-f3e4-4776-8c14-fcab2f740559 · outbound

This paper cites Learning to prompt for vision-language models.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Learning to prompt for vision-language models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.838027Z

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Observation 660e4989-aae3-4f3e-8fe0-735e1f600566 · outbound

This paper cites Not all features mat- ter: Enhancing few-shot clip with adaptive prior refinement.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation Not all features mat- ter: Enhancing few-shot clip with adaptive prior refinement

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.823183Z

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This paper cites The effects of regularization and data augmentation are class de- pendent.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation The effects of regularization and data augmentation are class de- pendent

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.809667Z

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Observation 6127d82a-d8dd-4f20-816f-2a290ced03a7 · outbound

This paper cites CLIP-Adapter: Better vision-language models with feature adapters.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation CLIP-Adapter: Better vision-language models with feature adapters

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:16.795423Z

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Observation b31530e7-b8a5-4313-8aea-e2087a8e3cb9 · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation VeRA: Vector-based Random Matrix Adaptation

Reference 54

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