Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models | Paper Citation Record

Observation 6a6dd822-3ea4-4472-93de-2611494d2460 · outbound

This paper cites TQCompressor: improving tensor decomposition methods in neural networks via permutations.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models TQCompressor: improving tensor decomposition methods in neural networks via permutations

Reference 1

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Observation 8819e99e-d458-49e4-a45b-e06a76dc0eab · outbound

This paper cites Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs

Reference 2

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Observation b77687af-de44-4d06-a759-2c6ed6341790 · outbound

This paper cites Sparse high rank adapters.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Sparse high rank adapters

Reference 3

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Observation 3858c61b-ee15-4b1f-9549-ee7a835e6555 · outbound

This paper cites Ether: Efficient finetuning of large-scale models with hyperplane reflections.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Ether: Efficient finetuning of large-scale models with hyperplane reflections

Reference 4

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Observation 2583bc89-197b-4e40-865d-168b68f060ba · outbound

This paper cites FouRA: Fourier Low Rank Adaptation.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models FouRA: Fourier Low Rank Adaptation

Reference 5

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This paper cites How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks).

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks)

Reference 6

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Observation 1009bb70-eb68-4e45-8914-75679ef4df4e · outbound

This paper cites Para: Personalizing text-to-image diffusion via parameter rank reduction.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Para: Personalizing text-to-image diffusion via parameter rank reduction

Reference 7

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Observation 19223f5c-5212-4bd4-8366-cca73275c52f · outbound

This paper cites Quanta: Efficient high- rank fine-tuning of llms with quantum-informed tensor adap- tation.arXiv preprint arXiv:2406.00132, 2024.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Quanta: Efficient high- rank fine-tuning of llms with quantum-informed tensor adap- tation.arXiv preprint arXiv:2406.00132, 2024

Reference 8

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Observation b537abc8-e252-45bf-b6d7-f02837e3d00e · outbound

This paper cites Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions.Foundations and Trends® in Machine Learning, 9(4-5):249–429, 2016.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions.Foundations and Trends® in Machine Learning, 9(4-5):249–429, 2016

Reference 9

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Observation 5ea189f6-f69e-4218-ae0c-0104616cf405 · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:01.124380Z

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Observation d07d1952-90dd-49d6-86e4-73e4d77432f1 · outbound

This paper cites A Note on LoRA.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models A Note on LoRA

Reference 11

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Observation a6f56cb8-97e8-450e-8fd9-c788a3f56c1e · outbound

This paper cites An image is worth one word: Personalizing text-to-image gener- ation using textual inversion.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models An image is worth one word: Personalizing text-to-image gener- ation using textual inversion

Reference 12

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Observation b38ffbb8-2d62-4f35-991d-be278cfe7f1a · outbound

This paper cites Parameter-efficient fine-tuning with discrete fourier transform.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Parameter-efficient fine-tuning with discrete fourier transform

Reference 13

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Observation 6ded2033-87ac-4ae7-a713-a5cc6d4bd87a · outbound

This paper cites Ultimate tensorization: compressing convolutional and FC layers alike.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Ultimate tensorization: compressing convolutional and FC layers alike

Reference 14

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This paper cites Deep learning book notation.https:// github.com/goodfeli/dlbook_notation.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Deep learning book notation.https:// github.com/goodfeli/dlbook_notation

Reference 15

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Observation e71f4815-9bcc-4f20-9d49-d882bc6979e9 · outbound

This paper cites Deep Learning.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Deep Learning

Reference 16

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Observation 7ba91cd3-7f01-4397-a325-52974a44dabe · outbound

This paper cites Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models.Advances in Neural Information Processing Sys- tems, 36, 2024.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models.Advances in Neural Information Processing Sys- tems, 36, 2024

Reference 17

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Observation 60b1effb-e722-4c57-b5e1-7580af3af80c · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Svdiff: Compact param- eter space for diffusion fine-tuning

Reference 18

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Observation 05705fd4-c5a3-4589-8d89-0a58810cfbea · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Parameter-efficient transfer learning for nlp

Reference 19

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Observation 8bb1e8cf-39aa-46a5-ba2d-0840b5766531 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models LoRA: Low-rank adaptation of large language models

Reference 20

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Observation 56075e05-4d36-454e-b1ef-72a6b53c10f8 · outbound

This paper cites SaRA: High-efficient diffusion model fine-tuning with progressive sparse low-rank adapta- tion.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models SaRA: High-efficient diffusion model fine-tuning with progressive sparse low-rank adapta- tion

Reference 21

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Observation 858af259-bcca-406e-be67-ed0dcbe761cf · outbound

This paper cites HiRA: Parameter-efficient hadamard high-rank adap- tation for large language models.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models HiRA: Parameter-efficient hadamard high-rank adap- tation for large language models

Reference 22

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Observation e4f9a1de-d333-4780-a5d5-88eda6fbf8d7 · outbound

This paper cites Fed- para: Low-rank hadamard product for communication- efficient federated learning.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Fed- para: Low-rank hadamard product for communication- efficient federated learning

Reference 23

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Observation c5fea690-b798-4434-9f2e-759afeff96a7 · outbound

This paper cites Fact: Factor-tuning for lightweight adaptation on vision transformer.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Fact: Factor-tuning for lightweight adaptation on vision transformer

Reference 24

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Observation 3e0e3021-98d9-430b-b78f-7467399ad0e4 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Vera: Vector-based random matrix adaptation

Reference 25

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Observation c5e551c5-5e07-478b-a0a9-3e1eba507f6a · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Multi-concept customization of text-to-image diffusion

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.966042Z

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Observation 7a143b29-53b5-4066-97af-b49f64829eff · outbound

This paper cites Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models

Reference 27

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Observation 63d8089f-33a2-4955-bffd-ea54b2855274 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models The power of scale for parameter-efficient prompt tuning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.955721Z

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Observation 04f13926-fddf-44fb-8450-7c6f338f7ec3 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 29

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Observation bb39628b-4450-443c-9ee3-d08e5580daf3 · outbound

This paper cites Prefix-tuning: Optimiz- ing continuous prompts for generation.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Prefix-tuning: Optimiz- ing continuous prompts for generation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.939014Z

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Observation f037159d-b1af-4744-9d31-f0cca63848a4 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models DoRA: Weight-decomposed low-rank adaptation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.929761Z

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Observation 95f6bf33-0051-4ac7-82f4-d8dcfd517ac8 · outbound

This paper cites Black, Adrian Weller, and Bernhard Sch¨olkopf.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Black, Adrian Weller, and Bernhard Sch¨olkopf

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.919796Z

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Observation 2e381c40-7454-4dfd-ab1f-2edfc8ad37b1 · outbound

This paper cites Badam: A memory ef- ficient full parameter optimization method for large language models.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Badam: A memory ef- ficient full parameter optimization method for large language models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.909921Z

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Observation 130f2222-5376-4632-b8ea-b5bc01f5090b · outbound

This paper cites Parameter efficient quasi-orthogonal fine- tuning via givens rotation.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Parameter efficient quasi-orthogonal fine- tuning via givens rotation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.899437Z

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Observation 48d3ee20-a446-4f6a-9b50-ab922e8e6cbc · outbound

This paper cites A tensorized transformer for language modeling.Advances in neural information pro- cessing systems, 32, 2019.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models A tensorized transformer for language modeling.Advances in neural information pro- cessing systems, 32, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.888655Z

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Observation 92fca436-f69a-4d2b-96b3-01448ded1052 · outbound

This paper cites Peft: State-of-the-art parameter-efficient fine-tuning meth- ods.https://github.com/huggingface/peft,.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Peft: State-of-the-art parameter-efficient fine-tuning meth- ods.https://github.com/huggingface/peft,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.877877Z

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Observation a2f7c604-7bb2-40d8-9aa9-663dd783541b · outbound

This paper cites Scaling recurrent models via orthogonal approximations in tensor trains.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Scaling recurrent models via orthogonal approximations in tensor trains

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.866957Z

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Observation f6d9008b-febd-4c86-be58-282514afcdc6 · outbound

This paper cites T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.855818Z

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Observation 14bb88e7-c2d2-4c8f-b156-8f78c9e8ded6 · outbound

This paper cites RoSA: Accurate parameter-efficient fine-tuning via robust adaptation.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models RoSA: Accurate parameter-efficient fine-tuning via robust adaptation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.844572Z

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Observation 0ef03101-d989-49e5-aaa7-de26a6b5a4d1 · outbound

This paper cites Tensorizing neural networks.Advances in neural information processing systems, 28, 2015.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Tensorizing neural networks.Advances in neural information processing systems, 28, 2015

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.833838Z

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Observation af6c4e28-7747-46ae-b13d-3dff15405b1a · outbound

This paper cites Dinov2: Learning robust visual features without supervision.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Dinov2: Learning robust visual features without supervision

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.823030Z

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Observation faff2605-0cff-4025-b572-b40a5c886e82 · outbound

This paper cites Tensor-train decomposition.SIAM Jour- nal on Scientific Computing, 33(5):2295–2317, 2011.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Tensor-train decomposition.SIAM Jour- nal on Scientific Computing, 33(5):2295–2317, 2011

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.812027Z

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Observation 8d2738c9-3023-4da2-8b71-aa7716a2df79 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Lisa: Layerwise importance sampling for memory-efficient large language model fine- tuning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.800064Z

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Observation 858586d2-3d14-44fc-9f33-990d5bf6a867 · outbound

This paper cites Compressing recurrent neural networks with tensor ring for action recognition.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Compressing recurrent neural networks with tensor ring for action recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.789216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0b33bc2-ff81-4bd2-874b-ed6e5d9a3d26 · outbound

This paper cites Tt-vit: Vision transformer compression using tensor-train decomposition.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Tt-vit: Vision transformer compression using tensor-train decomposition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.777825Z

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Observation 4a0cf128-9075-4f37-8fdd-41deabe708df · outbound

This paper cites SDXL: Improving latent diffusion models for high-resolution image synthesis.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models SDXL: Improving latent diffusion models for high-resolution image synthesis

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.766594Z

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Observation c1669275-59db-4d49-9f84-8f3c01d60812 · outbound

This paper cites Controlling text-to-image diffusion by orthogo- nal finetuning.Advances in Neural Information Processing Systems, 36:79320–79362, 2023.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Controlling text-to-image diffusion by orthogo- nal finetuning.Advances in Neural Information Processing Systems, 36:79320–79362, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.755832Z

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Observation c551d8df-7097-4b30-866e-0f371ab9ec68 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Learning transferable visual models from natural language supervi- sion

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Observation 4c611fe1-5dd7-4407-8349-bfb587806330 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Hierarchical Text-Conditional Image Generation with CLIP Latents

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Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.090065Z

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Observation 94f33bf7-8b2b-4946-a979-d796ca5a2047 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models High-resolution image synthesis with latent diffusion models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.738277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7a2217fe-188c-4142-b233-0ca7697bf42d · outbound

This paper cites RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.097136Z

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Observation ffff0a95-72e6-404d-8122-14f972c6d4f5 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.726988Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7bf309a2-93b4-4cb1-be7d-782cc3765281 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.715777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 680500bb-3ced-4640-8eec-42f76f81ee97 · outbound

This paper cites Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:24:00.278701Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f3c4d6fd-be5a-4f3b-9960-489cc99467e8 · outbound

This paper cites Convolutional tensor- train lstm for spatio-temporal learning.Advances in Neural Information Processing Systems, 33:13714–13726, 2020.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Convolutional tensor- train lstm for spatio-temporal learning.Advances in Neural Information Processing Systems, 33:13714–13726, 2020

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.704399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fa6a02f8-23bc-40e1-9258-ea15e02173bd · outbound

This paper cites SD-XL inpainting 0.1 model card.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models SD-XL inpainting 0.1 model card

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.693406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 850b332f-9dac-4f34-9144-cd3c5b89bc6c · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.119176Z

Source-reported events for the cited work

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Observation 02d4b957-3bc0-4082-80ed-7c05741befe8 · outbound

This paper cites Wide compression: Tensor ring nets.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Wide compression: Tensor ring nets

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.682926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8fd4e9f1-6b59-4147-889b-0dc535612ea8 · outbound

This paper cites Tedigan: Text-guided diverse face image generation and ma- nipulation.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Tedigan: Text-guided diverse face image generation and ma- nipulation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.126585Z

Source-reported events for the cited work

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Observation e2aed8f2-127b-4585-82ce-7bc133d06d1a · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in neural information processing systems, 34: 12077–12090, 2021.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in neural information processing systems, 34: 12077–12090, 2021

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.664819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b15989aa-9968-4010-89aa-56efed6776ca · outbound

This paper cites Difffit: Un- locking transferability of large diffusion models via sim- ple parameter-efficient fine-tuning.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Difffit: Un- locking transferability of large diffusion models via sim- ple parameter-efficient fine-tuning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.654140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e31d51dd-dbd6-4e49-99ca-7c09017b7fff · outbound

This paper cites Raphael: Text-to-image generation via large mixture of diffusion paths.Advances in Neural Information Processing Systems, 36, 2024.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Raphael: Text-to-image generation via large mixture of diffusion paths.Advances in Neural Information Processing Systems, 36, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.643388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cad315fc-19f6-4db0-bee0-fd1724ffe670 · outbound

This paper cites A Spectral Condition for Feature Learning.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models A Spectral Condition for Feature Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.140937Z

Source-reported events for the cited work

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Observation 349c4c0c-f72f-4bfa-a7e8-c08c8d42e1b9 · outbound

This paper cites Tensor- train recurrent neural networks for video classification.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Tensor- train recurrent neural networks for video classification

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.632159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1aa17ce3-65e2-4330-8c9f-76828189cf30 · outbound

This paper cites Loretta: Low-rank economic tensor-train adaptation for ultra-low-parameter fine-tuning of large language models.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Loretta: Low-rank economic tensor-train adaptation for ultra-low-parameter fine-tuning of large language models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.620878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 80eb7482-4cb2-4649-bf34-8d80e726826d · outbound

This paper cites Navigating text-to-image customization: From lyCORIS fine-tuning to model evaluation.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Navigating text-to-image customization: From lyCORIS fine-tuning to model evaluation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.610514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3f3e716c-8e31-4786-a3f6-246983204585 · outbound

This paper cites Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.156129Z

Source-reported events for the cited work

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Observation eea5ff5e-9eab-46e0-92aa-f67aeb37f217 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Adding conditional control to text-to-image diffusion models

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.600277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fdd47455-4512-4beb-82e5-2a6d8b4aadb3 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Adaptive budget allocation for parameter-efficient fine- tuning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.590158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9c612e0b-2a1f-4f50-86e2-1a39b53ba5fb · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models The unreasonable effectiveness of deep features as a perceptual metric

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.166604Z

Source-reported events for the cited work

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Observation 191c7142-8306-4229-a43d-4c9cd22d0780 · outbound

This paper cites Soda: Spectral 11 orthogonal decomposition adaptation for diffusion models.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Soda: Spectral 11 orthogonal decomposition adaptation for diffusion models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.572344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e800fed1-d05c-49cb-a0bb-22f2662e0cd8 · outbound

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

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Galore: Memory- efficient llm training by gradient low-rank projection

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.560863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0ee4f65-d09f-44f7-b31e-e6e0dd8783b3 · outbound

This paper cites Tensor Ring Decomposition.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Tensor Ring Decomposition

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.176763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27e57072-6b4c-40e1-86d7-7e6338884468 · outbound

This paper cites Scene parsing through ade20k dataset.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Scene parsing through ade20k dataset

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.180324Z

Source-reported events for the cited work

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Observation d0ba49e0-9228-4b7d-a064-0f298f3fa1a0 · outbound

This paper cites Time-varying lora: Towards effec- tive cross-domain fine-tuning of diffusion models.Advances in Neural Information Processing Systems, 37:73920–73951,.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Time-varying lora: Towards effec- tive cross-domain fine-tuning of diffusion models.Advances in Neural Information Processing Systems, 37:73920–73951,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.541558Z

Source-reported events for the cited work

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Observation a957194a-8efe-4c59-84ba-db9b81a9b977 · outbound

This paper cites Therefore, T[ i1 · · ·iD, j1 · · ·jD] = 0, sinceA d[id, jd,:,:] =0if id ̸=j d.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Therefore, T[ i1 · · ·iD, j1 · · ·jD] = 0, sinceA d[id, jd,:,:] =0if id ̸=j d

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.518232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2f8ec1d4-aed1-4e1e-aa55-8f125bac383c · outbound

This paper cites an unresolved cited work.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:24:00.507652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 16a54648-b84a-4a6a-8644-6be82c37af79 · outbound

This paper cites Details and more results in Sec.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Details and more results in Sec

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:24:00.529624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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