ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints | Paper Citation Record

Observation 5c234c6a-9b47-4196-b2fc-a966f981c41e · outbound

This paper cites GPT-4 Technical Report.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints GPT-4 Technical Report

Reference 1

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Observation ee869c9b-da67-4ebc-82a6-09b7c939ad15 · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 2

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Observation 23c9b2a3-1267-4c00-ad73-1ef7009a1214 · outbound

This paper cites Myvlm: Personalizing vlms for user-specific queries.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Myvlm: Personalizing vlms for user-specific queries

Reference 3

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Observation f8e92727-27ac-41d0-ac9a-f72ba639c0a9 · outbound

This paper cites SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models

Reference 4

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Observation 6f27059d-3527-4167-b794-182897c406d0 · outbound

This paper cites LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters

Reference 5

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Observation fa27f375-a4ab-48e8-8889-e17b9572ccc5 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints On the Opportunities and Risks of Foundation Models

Reference 6

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Observation 75f5e5aa-8a53-4907-8e6c-b555b4b07b9f · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints RT-1: Robotics Transformer for Real-World Control at Scale

Reference 7

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Observation 4f4056d0-b63f-44b6-807f-591ea4e8b77f · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 8

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Observation 5ef5792b-3d0c-4611-9b50-995630bbad5e · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models

Reference 9

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Observation b6490155-63c2-4a38-a2e3-c3c2323b88e2 · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

Reference 10

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Observation ab7e53b4-c33f-4c59-a6af-df6667ac0df6 · outbound

This paper cites Scaling vision transformers to 22 billion pa- rameters.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Scaling vision transformers to 22 billion pa- rameters

Reference 11

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Observation dcc4064f-b94e-4bd8-a295-ecac9061fcd1 · outbound

This paper cites an unresolved cited work.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Unresolved cited work

Reference 12

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Observation b4cce593-abb7-48d5-910f-1e96b753f4cf · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Qlora: Efficient finetuning of quantized llms

Reference 13

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Observation 835a32c6-3380-4183-a82d-e44e0fcc63b8 · outbound

This paper cites Domain-adversarial train- ing of neural networks.Journal of Machine Learning Re- search, 17(59):1–35, 2016.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Domain-adversarial train- ing of neural networks.Journal of Machine Learning Re- search, 17(59):1–35, 2016

Reference 14

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Observation c2d0d153-e954-4a9b-bfc6-11a9476ff131 · outbound

This paper cites Understanding the diffi- culty of training deep feedforward neural networks.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Understanding the diffi- culty of training deep feedforward neural networks

Reference 15

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Observation fc180810-b196-4f3d-8848-22f573ddbf3f · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 16

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Observation d702c769-1dc2-40a8-a9b6-098c8ceeaa44 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level perfor- mance on imagenet classification.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Delving deep into rectifiers: Surpassing human-level perfor- mance on imagenet classification

Reference 17

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Observation bf8366f4-3e6d-4614-ad0d-98627d186dd1 · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

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Observation 6ea9e949-39bc-4448-875e-577bddd7f795 · outbound

This paper cites A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA

Reference 19

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Observation 52dab9d3-41aa-4030-8057-f7e0c1107730 · outbound

This paper cites Elora: Efficient low-rank adaptation with random matrices.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Elora: Efficient low-rank adaptation with random matrices

Reference 20

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Observation bbe36e6d-c13a-4e40-bd3e-6a6bac78033f · outbound

This paper cites LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Reference 21

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Observation 36acfe59-8c65-401c-b1cb-0978683874ce · outbound

This paper cites Few- shot parameter-efficient fine-tuning is better and cheaper than in-context learning.Advances in Neural Information Processing Systems, 35:1950–1965, 2022.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Few- shot parameter-efficient fine-tuning is better and cheaper than in-context learning.Advances in Neural Information Processing Systems, 35:1950–1965, 2022

Reference 22

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Observation 55a544a0-f5e3-4a5e-826f-14391d209c5f · outbound

This paper cites Visual Instruction Tuning.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Visual Instruction Tuning

Reference 23

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Observation 6fe0b7e6-6d34-4530-92fb-810be6c27401 · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 24

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Observation 06cbedb8-3d22-490e-9327-d2c86299d80c · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 25

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Observation 6c6d9dff-99d6-44a8-ba62-ca1a2aaf9010 · outbound

This paper cites ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 26

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Observation 29b9672b-a3ac-429c-bdf2-3240b6811768 · outbound

This paper cites Decoupled Weight Decay Regularization.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Decoupled Weight Decay Regularization

Reference 27

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Observation 6a13e3fa-4f40-4b52-a793-bc724c2756ff · outbound

This paper cites A survey on lora of large language models.Frontiers of Computer Science, 19(1): 197605, 2025.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints A survey on lora of large language models.Frontiers of Computer Science, 19(1): 197605, 2025

Reference 28

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Observation c2ed777f-2b52-4747-9cb8-75d2c746ded8 · outbound

This paper cites PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Reference 29

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Observation 40b63a8c-0fbb-4c75-8424-e2f4bd0b9531 · outbound

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ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Unresolved cited work

Reference 30

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This paper cites RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation

Reference 31

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Observation ed80b4ef-e8e7-4346-a06e-6d1e7c79500d · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints DINOv2: Learning Robust Visual Features without Supervision

Reference 32

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This paper cites One initialization to rule them all: Fine- tuning via explained variance adaptation.arXiv preprint arXiv:2410.07170, 2024.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints One initialization to rule them all: Fine- tuning via explained variance adaptation.arXiv preprint arXiv:2410.07170, 2024

Reference 33

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This paper cites Learning Transferable Visual Models From Natural Language Supervision.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Learning Transferable Visual Models From Natural Language Supervision

Reference 34

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Observation 70712c98-11b2-447f-900f-25b271bb5778 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Sentence-bert: Sentence embeddings using siamese bert-networks

Reference 35

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Observation 696b09d9-6d1a-4109-b315-0414e3e681b6 · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints High-resolution image synthesis with latent diffusion models

Reference 36

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 37

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Observation 03c3926a-7039-4ff0-bb52-d69bd74e6947 · outbound

This paper cites Amazon product descriptions vlm, 2024.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Amazon product descriptions vlm, 2024

Reference 38

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Observation ed3a514f-5485-4fb8-ac8b-fa27d2a0d61b · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Deep coral: Correlation alignment for deep domain adaptation

Reference 39

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This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 40

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Observation 857d4822-843b-4e79-9ea4-69f62e61e879 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints LLaMA: Open and Efficient Foundation Language Models

Reference 41

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Observation 0e7472a0-ad2c-4c8f-924d-29a6f52833d1 · outbound

This paper cites Deep domain confusion: Maximizing for domain invariance.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Deep domain confusion: Maximizing for domain invariance

Reference 42

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Observation 0dd18b82-e0f4-430d-969b-b64a9f34699d · outbound

This paper cites Adversarial discriminative domain adaptation.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Adversarial discriminative domain adaptation

Reference 43

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Observation 2aef86c7-a2e4-4feb-9eb4-90706a9e43cf · outbound

This paper cites DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation

Reference 44

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Observation 17bc166a-6a6b-4971-a0a3-5020a0f34b41 · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:48:56.488054Z

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Observation ce71ade5-4c56-4a0e-9682-45c5f43e62a0 · outbound

This paper cites LoRA-GA: Low-Rank Adaptation with Gradient Approximation.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 46

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Observation fdc5decb-1990-4067-bb87-872ef814d707 · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Corda: Context-oriented decomposition adaptation of large language models for task-aware parameter-efficient fine- tuning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:48:56.260721Z

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Observation cc97c862-ff13-4c75-95e2-2951b59bd104 · outbound

This paper cites Chatglm: A family of large language models from glm-130b to glm-4 all tools.CoRR, 2024.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Chatglm: A family of large language models from glm-130b to glm-4 all tools.CoRR, 2024

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:48:55.978527Z

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Observation 0f4d1cf6-d851-4b4f-af91-0e8757c79a48 · outbound

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

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 49

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Observation 8ed0d4bb-2b3b-47aa-b68a-9afadb3b77dc · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 50

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Observation ddd8987f-ee39-44de-9f82-7dd5fcc24e44 · outbound

This paper cites Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices

Reference 51

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