MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression | Paper Citation Record

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This paper cites Quantizable transformers: Removing outliers by helping attention heads do nothing.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Quantizable transformers: Removing outliers by helping attention heads do nothing

Reference 1

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Observation 0c5ce7f7-090c-458d-9910-b2b4bd63a6a3 · outbound

This paper cites Cascade r-cnn: Delving into high quality object detection.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Cascade r-cnn: Delving into high quality object detection

Reference 2

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Observation 9326d636-c85b-4cc5-813d-b1cd8c2a3cfe · outbound

This paper cites End- to-end object detection with transformers.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression End- to-end object detection with transformers

Reference 3

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This paper cites Exploiting linear structure within con- Figure 5.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Exploiting linear structure within con- Figure 5

Reference 4

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This paper cites BERT: Pre-training of deep bidirectional trans- formers for language understanding.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression BERT: Pre-training of deep bidirectional trans- formers for language understanding

Reference 5

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Observation 190e6d5b-0e05-40fe-979d-fd3e50e3ade3 · outbound

This paper cites Towards accurate post- training quantization for vision transformer.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Towards accurate post- training quantization for vision transformer

Reference 6

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Observation 48693c59-6409-4a9f-a27a-1411bcf7f460 · outbound

This paper cites Learning to prune deep neural networks via layer-wise optimal brain surgeon.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Learning to prune deep neural networks via layer-wise optimal brain surgeon

Reference 7

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Observation 958ba43a-f6a8-4a85-8cfb-3c94deb429b0 · outbound

This paper cites HAWQ: Hessian aware quanti- zation of neural networks with mixed-precision.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression HAWQ: Hessian aware quanti- zation of neural networks with mixed-precision

Reference 8

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Observation 57f3748d-df36-4c3d-8527-bed830b13691 · outbound

This paper cites Hawq-v2: Hessian aware trace-weighted quantization of neural networks.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Hawq-v2: Hessian aware trace-weighted quantization of neural networks

Reference 9

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Observation fa2cd620-6067-4fc5-91cb-b7807f281cd6 · outbound

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

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression An image is worth 16x16 words: Transformers for image recognition at scale

Reference 10

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Observation 64a35166-2df6-4c9b-afab-7c78372ca436 · outbound

This paper cites Optq: Accurate quantization for generative pre- trained transformers.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Optq: Accurate quantization for generative pre- trained transformers

Reference 11

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Observation 73094ec3-5b3a-495d-9803-4af694c41b65 · outbound

This paper cites A survey of quan- tization methods for efficient neural network inference.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression A survey of quan- tization methods for efficient neural network inference

Reference 12

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Observation f52a7b5c-5e36-4d3e-9435-32d12bae03e9 · outbound

This paper cites Singular value decom- position and least squares solutions.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Singular value decom- position and least squares solutions

Reference 13

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Observation aee0837f-f3ce-4821-8899-d3924f327830 · outbound

This paper cites EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization

Reference 14

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Observation 36c91d7c-9a7c-413b-a847-d9c05395f839 · outbound

This paper cites Olive: Accelerating large language models via hardware- friendly outlier-victim pair quantization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Olive: Accelerating large language models via hardware- friendly outlier-victim pair quantization

Reference 15

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Observation c914f25d-cd06-4598-a5a4-98ee54a32c64 · outbound

This paper cites Hmq: Hardware friendly mixed precision quantization block for cnns.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Hmq: Hardware friendly mixed precision quantization block for cnns

Reference 16

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Observation ac2d280a-cd6d-4979-a965-d301c465f464 · outbound

This paper cites Mask r-cnn.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Mask r-cnn

Reference 17

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Observation 32d873e1-3e27-423a-9a6d-411b5f006b52 · outbound

This paper cites Language model compression with weighted low-rank factorization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Language model compression with weighted low-rank factorization

Reference 18

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Observation 39f645b2-7ac0-4a62-98c3-ea5d1ee93da3 · outbound

This paper cites Numerical optimizations for weighted low-rank estimation on language models.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Numerical optimizations for weighted low-rank estimation on language models

Reference 19

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Observation 0448a017-fb27-40df-9611-0a6c3d38faf3 · outbound

This paper cites Dynamic low-rank estimation for transformer-based language models.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Dynamic low-rank estimation for transformer-based language models

Reference 20

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This paper cites Complexity-aware layer-wise mixed-precision schemes with sqnr-based fast analysis.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Complexity-aware layer-wise mixed-precision schemes with sqnr-based fast analysis

Reference 21

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Observation 382876d7-e880-4ab3-aff7-36302f816d8c · outbound

This paper cites One-shot model for mixed-precision quantization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression One-shot model for mixed-precision quantization

Reference 23

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Observation 54039bf7-20e5-41cf-afcb-e7c704a9eee1 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 24

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Observation 1f900107-0a20-4326-bc67-9dccb6816bf3 · outbound

This paper cites BRECQ: Push- ing the limit of post-training quantization by block recon- struction.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression BRECQ: Push- ing the limit of post-training quantization by block recon- struction

Reference 25

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Observation 6f39fbbe-d068-4470-9e6f-d9ef8cba5cbc · outbound

This paper cites Repq- vit: Scale reparameterization for post-training quantization of vision transformers.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Repq- vit: Scale reparameterization for post-training quantization of vision transformers

Reference 26

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Observation c507d71a-5f93-4c64-bd26-0fb45e1492c2 · outbound

This paper cites Microsoft coco: Common objects in context.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Microsoft coco: Common objects in context

Reference 27

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Observation aac99d73-7ae5-4bca-bb7d-41c860aa9826 · outbound

This paper cites Pd-quant: Post-training quantiza- tion based on prediction difference metric.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Pd-quant: Post-training quantiza- tion based on prediction difference metric

Reference 28

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MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Noisyquant: Noisy bias-enhanced post-training activation quantization for vision transformers

Reference 29

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Observation d62957ab-4ac3-4c87-911c-c447cc661860 · outbound

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

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Swin transformer: Hierarchical vision transformer using shifted windows

Reference 30

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Observation 1c9a2afb-5b33-47d1-bcbe-552392358879 · outbound

This paper cites Post-training quantization for vision trans- former.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Post-training quantization for vision trans- former

Reference 31

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Observation 74a35830-6ca5-4b25-b38c-ab1ab28dc51b · outbound

This paper cites Up or down? adap- tive rounding for post-training quantization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Up or down? adap- tive rounding for post-training quantization

Reference 32

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Observation fff28c53-f68b-4c13-b4b4-1910bd97dd2d · outbound

This paper cites Loss aware post-training quantization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Loss aware post-training quantization

Reference 33

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Observation e5982f35-10c0-4f11-8981-2fc841a11360 · outbound

This paper cites Compressing pre- trained language models by matrix decomposition.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Compressing pre- trained language models by matrix decomposition

Reference 34

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Observation c5fdc61c-1b55-40d7-859e-15b32cd48c45 · outbound

This paper cites A Practical Mixed Precision Algorithm for Post-Training Quantization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression A Practical Mixed Precision Algorithm for Post-Training Quantization

Reference 35

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This paper cites OR-Tools, 2024.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression OR-Tools, 2024

Reference 36

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Observation d19a2c72-0439-46c6-8303-b0c1afe3200f · outbound

This paper cites Probabilistic weather forecasting with machine learn- ing.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Probabilistic weather forecasting with machine learn- ing

Reference 37

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Observation 0d0868c1-65fa-4f00-9701-aedebac066f1 · outbound

This paper cites LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation

Reference 38

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MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Unresolved cited work

Reference 39

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Observation 49b5519a-de36-4d59-b68e-d34d044c106b · outbound

This paper cites Imagenet large scale visual recognition challenge.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Imagenet large scale visual recognition challenge

Reference 40

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Observation 70ecd6f7-61ec-47e7-a7a7-5da3642b4412 · outbound

This paper cites Weighted low-rank ap- proximations.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Weighted low-rank ap- proximations

Reference 41

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Observation 5032eb93-9e85-449b-9433-d1dae40c3a14 · outbound

This paper cites Amp-vit: Optimizing vision transformer efficiency with adaptive mixed-precision post- training quantization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Amp-vit: Optimizing vision transformer efficiency with adaptive mixed-precision post- training quantization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:59.840379Z

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Observation c8ea6b4a-5643-49dd-96ab-7fc658e480e8 · outbound

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

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Training data-efficient image transformers & distillation through at- tention

Reference 43

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Observation 08666285-f918-469f-b094-d2b8fe7d9d1f · outbound

This paper cites Mixed precision dnns: All you need is a good parametrization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Mixed precision dnns: All you need is a good parametrization

Reference 44

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Observation 8e237831-8e26-44ca-9827-746bab21e338 · outbound

This paper cites Attention is all you need.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Attention is all you need

Reference 45

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Observation 96e3aa5b-7408-46e7-9fbc-1c95f1565a17 · outbound

This paper cites GLUE: A multi-task benchmark and analysis platform for natural language un- derstanding.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression GLUE: A multi-task benchmark and analysis platform for natural language un- derstanding

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:59.016518Z

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Observation 4bdfa9e1-909f-4ab1-83f6-219fd590df1c · outbound

This paper cites SVD- LLM: Truncation-aware singular value decomposition for large language model compression.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression SVD- LLM: Truncation-aware singular value decomposition for large language model compression

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:58.825439Z

Source-reported events for the cited work

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Observation 5b2ab7ce-4735-4351-88e7-d83d18b9594c · outbound

This paper cites QDrop: Randomly dropping quantization for extremely low-bit post-training quantization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression QDrop: Randomly dropping quantization for extremely low-bit post-training quantization

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:58.651727Z

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Observation 4ed7f6cc-a8a6-43c8-93f4-ae97fdf5d9cd · outbound

This paper cites Outlier sup- pression+: Accurate quantization of large language models by equivalent and effective shifting and scaling.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Outlier sup- pression+: Accurate quantization of large language models by equivalent and effective shifting and scaling

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:58.457105Z

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Observation 5a8a6135-5aab-40c2-97bc-7edc411f9246 · outbound

This paper cites Pytorch image models.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Pytorch image models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:58.272363Z

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Observation 9f6189f4-6960-4547-9d46-3948900d1179 · outbound

This paper cites Smoothquant: Accurate and ef- ficient post-training quantization for large language models.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Smoothquant: Accurate and ef- ficient post-training quantization for large language models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:58.040160Z

Source-reported events for the cited work

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Observation d25eb928-a8e1-41a0-894f-35d0f4323085 · outbound

This paper cites Patch- wise mixed-precision quantization of vision transformer.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Patch- wise mixed-precision quantization of vision transformer

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:57.880135Z

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Observation 7821308a-bc30-4af9-86f6-e092eecc344d · outbound

This paper cites Efficient low-rank back- propagation for vision transformer adaptation.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Efficient low-rank back- propagation for vision transformer adaptation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:57.697143Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0e554148-530c-445d-b33c-4374c99b78db · outbound

This paper cites Compressing transformers: fea- tures are low-rank, but weights are not! In Proceedings of the AAAI Conference on Artificial Intelligence, pages 11007– 11015, 2023.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Compressing transformers: fea- tures are low-rank, but weights are not! In Proceedings of the AAAI Conference on Artificial Intelligence, pages 11007– 11015, 2023

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:57.509909Z

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Observation bb927ecd-fcb4-44b6-bb32-f4c1bb2e1d36 · outbound

This paper cites Hessian- aware pruning and optimal neural implant.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Hessian- aware pruning and optimal neural implant

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:57.378613Z

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Observation 238b163c-1d14-4595-98ff-422a9cbb0e4a · outbound

This paper cites Ptq4vit: Post-training quantization for vi- sion transformers with twin uniform quantization.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Ptq4vit: Post-training quantization for vi- sion transformers with twin uniform quantization

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:57.237453Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b1a24dbb-21c8-4226-b023-9c8ce00167a9 · outbound

This paper cites ERQ: Error reduction for post-training quanti- zation of vision transformers.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression ERQ: Error reduction for post-training quanti- zation of vision transformers

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:57:57.082436Z

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Observation 4ed5ad3c-5949-4c67-a3bd-10938a35cdfa · outbound

This paper cites Towards accurate post-training quantization of vision transformers via error reduction.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Towards accurate post-training quantization of vision transformers via error reduction

Reference 58

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T17:57:56.919924Z

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Observation 4683f16c-5aa0-43ad-9182-afa694e22af2 · outbound

This paper cites an unresolved cited work.

MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:57:56.991726Z

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