Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter | Paper Citation Record

Observation de57b22c-8c5d-4ce1-ae76-c300043be162 · outbound

This paper cites What does a platypus look like? generating customized prompts for zero-shot image classification,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter What does a platypus look like? generating customized prompts for zero-shot image classification,

Reference 1

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Observation b5e08e7f-3eba-46f1-9b5d-f3e3be85d263 · outbound

This paper cites Graphadapter: Tuning vision-language models with dual knowledge graph,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Graphadapter: Tuning vision-language models with dual knowledge graph,

Reference 2

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Observation 74d72df5-7c83-4d29-8944-be26b673665b · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Learning transferable visual models from natural language supervision,

Reference 3

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 4

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This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 5

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Observation 72d697b9-8cc2-437f-8fb1-546eabb3a106 · outbound

This paper cites Calip: Zero-shot enhancement of clip with parameter-free attention,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Calip: Zero-shot enhancement of clip with parameter-free attention,

Reference 6

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Observation 9cff2d18-22bc-4981-a1e6-bda52d3e1969 · outbound

This paper cites SuS-X: Training-free name- only transfer of vision-language models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter SuS-X: Training-free name- only transfer of vision-language models,

Reference 7

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Observation eb5ae94e-4d6b-4a5b-a12e-7180630be493 · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Learning to prompt for vision- language models,

Reference 8

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Observation 4208fa22-5514-4f0e-92d7-e6d278b42f62 · outbound

This paper cites How does fine-tuning impact out-of-distribution de- tection for vision-language models?.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter How does fine-tuning impact out-of-distribution de- tection for vision-language models?

Reference 9

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Observation d0e263c2-3ae8-4625-b36a-39159159e592 · outbound

This paper cites Ifseg: Image-free se- mantic segmentation via vision-language model,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Ifseg: Image-free se- mantic segmentation via vision-language model,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.932912Z

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Observation fbd45ba8-3991-45f8-a6bf-0005b08040d8 · outbound

This paper cites Robust fine-tuning of zero-shot models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Robust fine-tuning of zero-shot models,

Reference 11

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Observation 2763ae62-1eda-4fbc-97c6-8985e0517100 · outbound

This paper cites Fd-align: feature discrimination alignment for fine-tuning pre-trained models in few-shot learning,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Fd-align: feature discrimination alignment for fine-tuning pre-trained models in few-shot learning,

Reference 12

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Observation bc8d34c3-d22d-4dc5-b6c7-696aaf6888fb · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Conditional prompt learning for vision-language models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.894572Z

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Observation c763bd71-9463-46b4-9014-ed41b54fc979 · outbound

This paper cites Prompt distribution learning,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Prompt distribution learning,

Reference 14

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This paper cites Bi-modality individual- aware prompt tuning for visual-language model,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Bi-modality individual- aware prompt tuning for visual-language model,

Reference 15

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter CLIP-Adapter: Better vision-language models with feature adapters,

Reference 16

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Observation 621dcda8-7c8e-43b0-b1a5-b98227ca29b1 · outbound

This paper cites Tip-Adapter: Training-free adaption of clip for few-shot classification,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Tip-Adapter: Training-free adaption of clip for few-shot classification,

Reference 17

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Observation a1bc1388-fb2d-4e51-b599-c740d65bb528 · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Task residual for tuning vision-language models,

Reference 18

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Observation b9861b66-adeb-44f0-85b4-dfcd3d4b40d3 · outbound

This paper cites Tun- ing vision-language models with multiple prototypes clustering,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Tun- ing vision-language models with multiple prototypes clustering,

Reference 19

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Observation a0abee12-5fb7-48c1-a618-e54d1662dd74 · outbound

This paper cites Mma: Multi-modal adapter for vision-language models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Mma: Multi-modal adapter for vision-language models,

Reference 20

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Observation 7ae46e8a-a692-4ad9-b72a-24c1835d4e3a · outbound

This paper cites Maple: Multi-modal prompt learning,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Maple: Multi-modal prompt learning,

Reference 21

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This paper cites Bayesian prompt learn- ing for image-language model generalization,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Bayesian prompt learn- ing for image-language model generalization,

Reference 22

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Observation 17fcd0a4-19d9-4ce1-aa73-15cc0844ee7f · outbound

This paper cites Promp- tkd: Unsupervised prompt distillation for vision-language models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Promp- tkd: Unsupervised prompt distillation for vision-language models,

Reference 23

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This paper cites Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners,

Reference 24

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Observation 19b64a73-6e9b-4cdc-b36f-13b7b390dae2 · outbound

This paper cites Textrefiner: Internal visual feature as efficient refiner for vision-language models prompt tun- ing,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Textrefiner: Internal visual feature as efficient refiner for vision-language models prompt tun- ing,

Reference 25

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Observation 65f12372-ebec-4c7d-82ed-b3aa8b8175fa · outbound

This paper cites Learning with enriched inductive biases for vision-language models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Learning with enriched inductive biases for vision-language models,

Reference 26

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Observation d881c119-adbc-45fe-869e-a1fc71f54473 · outbound

This paper cites HeGraphAdapter: Tuning Multi-Modal Vision-Language Models with Heterogeneous Graph Adapter.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter HeGraphAdapter: Tuning Multi-Modal Vision-Language Models with Heterogeneous Graph Adapter

Reference 27

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Observation 9bdf151f-4e73-4bc4-9f78-7f7775487183 · outbound

This paper cites Language models are few-shot learners,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Language models are few-shot learners,

Reference 28

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Observation ac44985e-ee15-4ba4-9d0c-8c88891deb45 · outbound

This paper cites Amu-tuning: Effective logit bias for clip-based few-shot learning,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Amu-tuning: Effective logit bias for clip-based few-shot learning,

Reference 29

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Observation 99ecb6fa-9f42-4b1e-ae9c-dcb7d915cf17 · outbound

This paper cites Bayesian exploration of pre- trained models for low-shot image classification,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Bayesian exploration of pre- trained models for low-shot image classification,

Reference 30

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Observation c0b3f15c-e064-4935-b285-7d8f60ec83b9 · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter An empirical study of training self- supervised vision transformers,

Reference 31

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Observation dead694a-1752-4f15-87dc-85c93540e45f · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Emerging properties in self-supervised vision transformers,

Reference 32

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Observation cfca35aa-fc47-4649-b931-9b8e6f41b300 · outbound

This paper cites Confidence estimation of classification based on the distribution of the neural network output layer.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Confidence estimation of classification based on the distribution of the neural network output layer

Reference 33

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Observation a0fe5ce4-2127-426d-ae79-9ca282f58bf2 · outbound

This paper cites Zero-shot text-to-image generation,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Zero-shot text-to-image generation,

Reference 34

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This paper cites Distribution-aware prompt tuning for vision-language models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Distribution-aware prompt tuning for vision-language models,

Reference 35

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Observation 626481ba-2516-4635-8149-4665a3e7d232 · outbound

This paper cites Make prompts adaptable: Bayesian modeling for vision-language prompt learning with data-dependent prior,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Make prompts adaptable: Bayesian modeling for vision-language prompt learning with data-dependent prior,

Reference 36

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Observation f1acd2aa-6bac-4df7-82e7-c83a06151fec · outbound

This paper cites Any- shift prompting for generalization over distributions,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Any- shift prompting for generalization over distributions,

Reference 37

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Observation 07151d1e-b922-4dce-803b-e3022a1ab53c · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Semi-supervised classification with graph convolutional networks,

Reference 38

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This paper cites Graph attention networks,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Graph attention networks,

Reference 39

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This paper cites Label propagation for zero-shot classifi- cation with vision-language models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Label propagation for zero-shot classifi- cation with vision-language models,

Reference 40

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Observation 6c46f470-ba1a-4b70-a198-0cfa90c299d1 · outbound

This paper cites Efficient and context- aware label propagation for zero-/few-shot training-free adaptation of vision-language model,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Efficient and context- aware label propagation for zero-/few-shot training-free adaptation of vision-language model,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.566841Z

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This paper cites On vertex, edge, and vertex-edge random graphs,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter On vertex, edge, and vertex-edge random graphs,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.554720Z

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This paper cites Awt: Transferring vision- language models via augmentation, weighting, and transportation,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Awt: Transferring vision- language models via augmentation, weighting, and transportation,

Reference 43

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Observation e08ca986-1356-4d83-9d48-6758526b53ed · outbound

This paper cites Robust graph convolutional networks against adversarial attacks,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Robust graph convolutional networks against adversarial attacks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.529188Z

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This paper cites Auto-encoding variational bayes,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Auto-encoding variational bayes,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.515562Z

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Observation b9d979d3-05d2-428a-aa2a-46163a9db8ca · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter ImageNet: A large-scale hierarchical image database,

Reference 46

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Observation 72d3fb14-5ade-43ae-9545-5e6bb452a3de · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter 3D object representations for fine-grained categorization,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.489414Z

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Observation 4659af24-202c-4b8b-a348-e08f06da3468 · outbound

This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.475953Z

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Observation afb68314-928b-44ca-a59d-7183acb62f16 · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 49

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Automated flower classification over a large number of classes,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.463296Z

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Observation d03c106e-142c-4aef-9d9e-53cbd0dbbc04 · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Food-101–Mining discriminative components with random forests,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.450374Z

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Observation 3247b07b-9a99-49fa-b3a1-4a5f47ccdc93 · outbound

This paper cites Describing textures in the wild,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Describing textures in the wild,

Reference 52

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Observation d122b2e1-e46f-4327-8293-1482adc8fd2b · outbound

This paper cites EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classi- fication,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classi- fication,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.422467Z

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Observation ec090b06-3f27-4a74-bf04-9ed5282e7c15 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Fine-Grained Visual Classification of Aircraft

Reference 54

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This paper cites Cats and dogs,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Cats and dogs,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.410115Z

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Observation 22f56489-b073-46cd-8ea3-a26b2dc95e2f · outbound

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter SUN database: Large-scale scene recognition from abbey to zoo,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.397530Z

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Observation cca564c6-c9bb-46d0-bd36-6ebba2a5625f · outbound

This paper cites Do ImageNet classifiers generalize to ImageNet?.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Do ImageNet classifiers generalize to ImageNet?

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.384108Z

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This paper cites Learning robust global representations by penalizing local predictive power,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Learning robust global representations by penalizing local predictive power,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.370319Z

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This paper cites Decoupled weight decay regularization,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Decoupled weight decay regularization,

Reference 59

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This paper cites Fast and accurate deep network learning by exponential linear units (elus),.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Fast and accurate deep network learning by exponential linear units (elus),

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.349481Z

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Observation 2daa355c-8c49-41a4-b238-72345483e0d3 · outbound

This paper cites Deep sparse rectifier neural networks,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Deep sparse rectifier neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.336877Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5c3ada0e-c6a9-4576-9016-7a20791c7194 · outbound

This paper cites Dual memory networks: A versatile adaptation approach for vision-language models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Dual memory networks: A versatile adaptation approach for vision-language models,

Reference 62

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Observation 67ccdd2a-3835-41dd-a1a0-0a25b6aa03be · outbound

This paper cites Self-regulating prompts: Foundational model adaptation without forgetting,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Self-regulating prompts: Foundational model adaptation without forgetting,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.316158Z

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Observation 6be451df-fd56-4d00-9d65-6e7e9d3a64a1 · outbound

This paper cites Mmrl: Multi-modal representation learning for vision-language models,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Mmrl: Multi-modal representation learning for vision-language models,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:40:20.302423Z

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Observation 23c56b72-9680-43c8-ac15-4be9e45861b3 · outbound

This paper cites Deep residual learning for image recognition,.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Deep residual learning for image recognition,

Reference 65

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

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 66

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Observation 3a0cf025-8fdd-4c61-9bcc-f14d3d026119 · outbound

This paper cites Visualizing data using t-sne.

Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter Visualizing data using t-sne

Reference 67

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