ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts | Paper Citation Record

Observation 6e664dac-3d25-4d43-9123-91ac1cf699f0 · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

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Observation 4ab27bef-0275-4cc8-a395-71fe2031dc29 · outbound

This paper cites DeepMind Lab.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts DeepMind Lab

Reference 2

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Observation 11d51109-2b75-41e7-a8ef-1704610f2d22 · outbound

This paper cites One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

Reference 3

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Observation 8695a79d-4cc9-479a-8809-3cae22053283 · outbound

This paper cites Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets

Reference 4

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Observation ff9cc4b0-121d-450c-99b3-87ac501de290 · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recogni- tion.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Adaptformer: Adapting vision transformers for scalable visual recogni- tion

Reference 5

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Observation 01b5b844-ef0d-4366-a92c-b6ccc0bd9ae4 · outbound

This paper cites An Empirical Study of Training Self-Supervised Vision Transformers.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts An Empirical Study of Training Self-Supervised Vision Transformers

Reference 6

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Observation fa848fcd-d12f-4b26-9d1f-af961ee1f832 · outbound

This paper cites Remote sens- ing image scene classification: Benchmark and state of the art.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Remote sens- ing image scene classification: Benchmark and state of the art

Reference 7

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Observation 4674ec86-2a96-4724-a196-c675d9eb021d · outbound

This paper cites Rethinking embedding coupling in pre-trained language models.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Rethinking embedding coupling in pre-trained language models

Reference 8

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Observation 558d62d5-cd8c-407b-8aa8-f3f9509272f8 · outbound

This paper cites Describing textures in the wild.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Describing textures in the wild

Reference 9

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Observation 1ca176f7-76ea-43ab-9d4f-b9a25b7d87ec · outbound

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

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Imagenet: A large-scale hierarchical image database

Reference 10

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Observation eb4142b5-09cc-48f1-b005-3fa2070961ea · outbound

This paper cites Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks

Reference 11

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Observation 35ba3834-4d92-4999-95d3-79ef9d627409 · outbound

This paper cites RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

Reference 12

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Observation 4f6e395e-1087-43b8-afe2-da5abd71869d · outbound

This paper cites Diverse branch block: Building a convolution as an inception-like unit.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Diverse branch block: Building a convolution as an inception-like unit

Reference 13

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Observation 7ab871a2-94e1-41a3-82b1-1344cf608ad8 · outbound

This paper cites Repvgg: Making vgg-style convnets great again.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Repvgg: Making vgg-style convnets great again

Reference 14

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This paper cites Repmlpnet: Hierarchical vi- sion mlp with re-parameterized locality.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Repmlpnet: Hierarchical vi- sion mlp with re-parameterized locality

Reference 15

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Observation ad89e490-c3f5-405c-87f6-853e43195c70 · outbound

This paper cites Effi- cient adaptation of large vision transformer via adapter re- composing.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Effi- cient adaptation of large vision transformer via adapter re- composing

Reference 16

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Observation 3c324e1d-8dc8-4458-b3d7-da118f6b3d01 · outbound

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

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts An image is worth 16x16 words: Transformers for image recognition at scale

Reference 17

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Observation 981ece85-833b-4341-8346-14480bea05d7 · outbound

This paper cites One-shot learning of object categories.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts One-shot learning of object categories

Reference 18

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Observation 097f144c-fb45-4c40-a3f7-5789ba68e68d · outbound

This paper cites Fine-grained car detection for visual census estimation.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Fine-grained car detection for visual census estimation

Reference 19

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Observation 785d848b-4fe9-4910-9efc-49eea11483fc · outbound

This paper cites Vision meets robotics: The kitti dataset.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Vision meets robotics: The kitti dataset

Reference 20

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Observation 39f290f0-d332-4d81-a4d3-eba53ddfea01 · outbound

This paper cites Kaggle diabetic retinopathy detection compe- tition report.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Kaggle diabetic retinopathy detection compe- tition report

Reference 21

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Observation fffa2c1e-5235-46c9-85bf-ce8ce001bfdf · outbound

This paper cites Deep residual learning for image recognition.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Deep residual learning for image recognition

Reference 22

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Observation 91dd390f-1966-46f8-8d23-7d01826c15c7 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Momentum contrast for unsupervised visual rep- resentation learning

Reference 23

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Observation 629d94f7-f354-4e11-bf27-4658d9cae41b · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Masked Autoencoders Are Scalable Vision Learners

Reference 24

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

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 25

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Observation 9e1951a7-67b8-47ae-bf5e-0a56ec830cd5 · outbound

This paper cites Compacter: Effi- cient low-rank hypercomplex adapter layers.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Compacter: Effi- cient low-rank hypercomplex adapter layers

Reference 26

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Observation b2a50d71-ff7e-4031-9769-dd8ad54003be · outbound

This paper cites Parameter-efficient transfer learning for nlp.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Parameter-efficient transfer learning for nlp

Reference 27

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Observation cd846ffe-7100-463d-9c94-0e1774b8638c · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts LoRA: Low-Rank Adaptation of Large Language Models

Reference 28

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Observation 1ce50cc2-7327-4816-9b1a-2a5dd2d167dc · outbound

This paper cites Visual prompt tuning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Visual prompt tuning

Reference 29

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Observation 56e9a360-373a-4544-b0cf-0a7cd82e8214 · outbound

This paper cites Res- tuning: A flexible and efficient tuning paradigm via unbind- ing tuner from backbone.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Res- tuning: A flexible and efficient tuning paradigm via unbind- ing tuner from backbone

Reference 30

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This paper cites Convolutional Bypasses Are Better Vision Transformer Adapters.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Convolutional Bypasses Are Better Vision Transformer Adapters

Reference 31

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This paper cites Clevr: A diagnostic dataset for compositional language and elementary visual reasoning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Reference 32

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This paper cites Novel dataset for fine-grained image categorization: Stanford dogs.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Novel dataset for fine-grained image categorization: Stanford dogs

Reference 33

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Observation 9b018d63-f997-4b0c-9b12-b1000b877a7c · outbound

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

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Learning multiple layers of features from tiny images

Reference 34

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Observation dcda7cfe-f371-49a5-b84a-61aa930375d5 · outbound

This paper cites Hmdb: a large video database for human motion recognition.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Hmdb: a large video database for human motion recognition

Reference 35

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This paper cites Learn- ing methods for generic object recognition with invari- ance to pose and lighting.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Learn- ing methods for generic object recognition with invari- ance to pose and lighting

Reference 36

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Observation 089a532c-a6cc-4226-a200-54c1c9be91c6 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts The power of scale for parameter-efficient prompt tuning

Reference 37

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Observation 5540cee6-56d0-42c8-b50c-9aa278579d43 · outbound

This paper cites Measuring the Intrinsic Dimension of Objective Landscapes.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Measuring the Intrinsic Dimension of Objective Landscapes

Reference 38

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Observation a1e3b14a-56ea-45ba-b50e-1fd31922f71a · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 39

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Observation 11c2351d-9ce3-4fda-bb57-0878e70f518c · outbound

This paper cites AS-MLP: An Axial Shifted MLP Architecture for Vision.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts AS-MLP: An Axial Shifted MLP Architecture for Vision

Reference 40

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This paper cites Scaling & shifting your features: A new baseline for efficient model tuning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Scaling & shifting your features: A new baseline for efficient model tuning

Reference 41

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Observation 8bc0ee52-6236-47b3-9b7e-e333dbd55a01 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in nat- ural language processing.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Pre-train, prompt, and predict: A systematic survey of prompting methods in nat- ural language processing

Reference 42

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Observation 173c2418-9c9e-47d2-9dc2-2a3656ee7c77 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Swin transformer: Hierarchical vision transformer using shifted windows

Reference 43

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Observation e2ed28a8-192b-4121-8137-a5b3141d7215 · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Swin transformer v2: Scaling up capacity and resolution

Reference 44

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Observation 27d89c97-2674-42e4-903c-ebf13fe348fd · outbound

This paper cites A convnet for the 2020s.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts A convnet for the 2020s

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.682241Z

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Observation 92c34ea8-61aa-4a58-8506-8d323df1c812 · outbound

This paper cites Decoupled weight decay regularization.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Decoupled weight decay regularization

Reference 46

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Observation f11b0a61-66ec-42c9-a0e9-2e384c08b83d · outbound

This paper cites Towards Efficient Visual Adaption via Structural Re-parameterization.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Towards Efficient Visual Adaption via Structural Re-parameterization

Reference 47

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Observation f2e5bf39-01d1-4c65-a717-e1e56b9c9b85 · outbound

This paper cites dsprites: Disentanglement testing sprites dataset,.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts dsprites: Disentanglement testing sprites dataset,

Reference 48

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Observation 6ab37b4a-d64a-4f46-8be5-86794d2e1110 · outbound

This paper cites Time-, Memory- and Parameter-Efficient Visual Adaptation.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Time-, Memory- and Parameter-Efficient Visual Adaptation

Reference 49

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Observation 2370cfd7-d674-4b77-8127-7f393d9f4d08 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Reading digits in natural images with unsupervised feature learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.634615Z

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Observation 328ddb23-d0e5-4e25-95aa-926cf69b95e8 · outbound

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

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Automated flower classification over a large number of classes

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.619546Z

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Observation 0dcf479b-5272-4b42-8d55-29ec0ccc2f83 · outbound

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

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Automated flower classification over a large number of classes

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.602790Z

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Observation 3152d619-2bd9-4fc9-9379-6ae70365ad70 · outbound

This paper cites Cats and dogs.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Cats and dogs

Reference 53

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Observation ff68636d-6fb2-45a3-80b1-fc5b1a783508 · outbound

This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 54

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This paper cites Imagenet-21k pretraining for the masses.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Imagenet-21k pretraining for the masses

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.565296Z

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Observation e335093f-77eb-4729-b13e-aa0affd30345 · outbound

This paper cites AdapterDrop: On the Efficiency of Adapters in Transformers.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts AdapterDrop: On the Efficiency of Adapters in Transformers

Reference 56

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Observation 15a8362b-d8f4-4a12-be1f-c68d25e5b21e · outbound

This paper cites AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Reference 57

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Observation f7da9432-2148-49c6-8aa6-00cc97308899 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Dropout: a simple way to prevent neural networks from overfitting

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.548462Z

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Observation 99949a2a-8f3c-4ff1-9f0b-063b247416ed · outbound

This paper cites Lst: Lad- der side-tuning for parameter and memory efficient trans- fer learning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Lst: Lad- der side-tuning for parameter and memory efficient trans- fer learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.532049Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cefcc746-cada-43a7-b3c0-6257b017d033 · outbound

This paper cites MLP-Mixer: An all-MLP Architecture for Vision.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts MLP-Mixer: An all-MLP Architecture for Vision

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T17:59:15.744703Z

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Observation 889f3bcf-f342-48be-9d90-57807271e37c · outbound

This paper cites Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.514211Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 76f1448f-605d-459f-ae9e-c1e31ba34d3b · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Training data-efficient image transformers & distillation through at- tention

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.498861Z

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Observation 43e537e2-ebda-4948-bef8-293db35c33e5 · outbound

This paper cites Visualizing data using t-sne.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Visualizing data using t-sne

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T17:59:15.760583Z

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Observation 4a0118cb-c0d2-4cb4-9350-276caf613e95 · outbound

This paper cites Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.472940Z

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Observation 000ab995-e4ef-4df4-8a83-2e2d045abdd1 · outbound

This paper cites Attention is all you need.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Attention is all you need

Reference 65

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Observation 47ed83b1-3b04-4fcd-bd2d-36ecee1bc630 · outbound

This paper cites Rotation equivariant cnns for digital pathology.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Rotation equivariant cnns for digital pathology

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.446982Z

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Observation c2536ddd-2fc8-4ea5-ac68-a1b8f2c18447 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts The caltech-ucsd birds-200-2011 dataset

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.430868Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a718869f-de8b-4fd1-8d24-d73faf318fda · outbound

This paper cites BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T17:59:15.788752Z

Source-reported events for the cited work

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Observation 34e65d68-8fe8-495f-9e8c-8dbdf7d1f22a · outbound

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

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Sun database: Large-scale scene recognition from abbey to zoo

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.416019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7c0dc4b0-6d1f-4d42-8c5a-9ed45d5b9ba3 · outbound

This paper cites Adapter is All You Need for Tuning Visual Tasks.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Adapter is All You Need for Tuning Visual Tasks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T17:59:15.799186Z

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Observation bb3d538e-c251-454b-af49-738e671a6a7a · outbound

This paper cites DiracNets: Training Very Deep Neural Networks Without Skip-Connections.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts DiracNets: Training Very Deep Neural Networks Without Skip-Connections

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:59:15.965201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c617ce40-28db-4237-867b-dd659f60de5d · outbound

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

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.400701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 27e5cf9f-baf2-4982-9166-ada862c9a230 · outbound

This paper cites A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T17:59:15.814734Z

Source-reported events for the cited work

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Observation b65c9371-e074-463c-a4a4-cef492c7a8b4 · outbound

This paper cites Scaling vision transformers.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Scaling vision transformers

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.385007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fa9b415d-773c-4cb5-a481-f7312189e689 · outbound

This paper cites Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T17:59:15.824792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba1d6aa7-ac52-433d-bfa1-f55e1fae399d · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine- tuning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Adaptive budget allocation for parameter-efficient fine- tuning

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.369514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f618d415-bf43-4314-b509-6ae59ad03f79 · outbound

This paper cites Revisiting Few-sample BERT Fine-tuning.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Revisiting Few-sample BERT Fine-tuning

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T17:59:15.834049Z

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Observation bd4a546c-da8d-4595-ac13-31dd01c16cb8 · outbound

This paper cites Neural Prompt Search.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Neural Prompt Search

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-11T17:59:15.838420Z

Source-reported events for the cited work

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Observation a0e7e1c8-6bd5-4491-9385-69aa0f90c83a · outbound

This paper cites Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-11T17:59:15.843109Z

Source-reported events for the cited work

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Observation 76c82519-e998-464c-9fb2-1bc60c1d0a59 · outbound

This paper cites Convolution meets lora: Parameter efficient finetuning for segment anything model.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Convolution meets lora: Parameter efficient finetuning for segment anything model

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:59:16.343793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 437da8bf-e347-4ec2-b726-0c4e75bcd7dc · outbound

This paper cites Group Mean.

ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts Group Mean

Reference 81

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T17:59:16.327052Z

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