SLORR: Simple and Efficient In-Training Low-Rank Regularization | Paper Citation Record

Observation eb9a9254-eddc-48ce-89e8-e7b4884c15f9 · outbound

This paper cites Ellie Pavlick and Tom Kwiatkowski.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Ellie Pavlick and Tom Kwiatkowski

Reference 1

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Observation 07ed930f-cd79-4d43-9d40-5f9737614fce · outbound

This paper cites Exploiting linear structure within convolutional networks for efficient evaluation.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Exploiting linear structure within convolutional networks for efficient evaluation

Reference 2

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Observation 380aab04-d2d5-4387-80db-33b475a9be26 · outbound

This paper cites Speeding up Convolutional Neural Networks with Low Rank Expansions.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Speeding up Convolutional Neural Networks with Low Rank Expansions

Reference 3

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Observation aee6e9d2-c4af-4b65-b36d-9c19a9f2640e · outbound

This paper cites DRONE: Data- aware Low-rank Compression for Large NLP Models.

SLORR: Simple and Efficient In-Training Low-Rank Regularization DRONE: Data- aware Low-rank Compression for Large NLP Models

Reference 4

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Observation 5a53f688-44c4-44ff-b548-f80cb0f7fd98 · outbound

This paper cites Alvarez and Mathieu Salzmann.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Alvarez and Mathieu Salzmann

Reference 5

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Observation 1bb3f8fb-cfae-490e-9fe4-b4a809a5cc21 · outbound

This paper cites TRP: Trained Rank Pruning for Efficient Deep Neural Networks.

SLORR: Simple and Efficient In-Training Low-Rank Regularization TRP: Trained Rank Pruning for Efficient Deep Neural Networks

Reference 6

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Observation 013c8d2e-4f52-44a6-ad81-5c61d8349a3f · outbound

This paper cites Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

Reference 7

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Observation 30a3c760-7a25-47c4-bc63-7bfbc195d928 · outbound

This paper cites Structure-Preserving Network Com- pression Via Low-Rank Induced Training Through Linear Layers Composition.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Structure-Preserving Network Com- pression Via Low-Rank Induced Training Through Linear Layers Composition

Reference 8

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Observation 370d6755-bafb-41fe-8939-5d917f69de40 · outbound

This paper cites Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training

Reference 9

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Observation 81ea7646-0ea3-48ef-812e-5a7eec8ab62f · outbound

This paper cites an unresolved cited work.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Unresolved cited work

Reference 10

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Observation 8b921733-cb71-4a64-9b95-e4d8368d3971 · outbound

This paper cites author Dong, W.

SLORR: Simple and Efficient In-Training Low-Rank Regularization author Dong, W

Reference 11

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Observation dcbae4c7-579c-400a-865f-614ca2c136ee · outbound

This paper cites Deep residual learning for image recognition.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Deep residual learning for image recognition

Reference 12

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Observation dce88f5c-65d1-4670-ac7c-97968a5ce2c7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SLORR: Simple and Efficient In-Training Low-Rank Regularization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation ea82edbe-6cb0-4d80-8d18-5d0a61b664ca · outbound

This paper cites From Low Rank Gradient Subspace Stabi- lization to Low-Rank Weights: Observations, Theories, and Applications.

SLORR: Simple and Efficient In-Training Low-Rank Regularization From Low Rank Gradient Subspace Stabi- lization to Low-Rank Weights: Observations, Theories, and Applications

Reference 14

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Observation 288e73af-7582-4912-ad76-d77aaab1aa62 · outbound

This paper cites Johns Hopkins University Press, Baltimore, MD (2013).

SLORR: Simple and Efficient In-Training Low-Rank Regularization Johns Hopkins University Press, Baltimore, MD (2013)

Reference 15

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Observation a637f4a5-54ef-4469-ad10-3774d0afcf42 · outbound

This paper cites Gradient-based learning applied to document recognition.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Gradient-based learning applied to document recognition

Reference 16

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Observation 47cec078-594b-47bb-8d26-fd3e014a3c15 · outbound

This paper cites Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives

Reference 17

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Observation ee31107e-1232-455e-a514-4abac3014066 · outbound

This paper cites SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression.

SLORR: Simple and Efficient In-Training Low-Rank Regularization SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

Reference 18

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Observation 8ec3fd2d-0e37-4a45-91bf-585c01d25535 · outbound

This paper cites Anderson, B.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Anderson, B

Reference 19

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Observation 5ede3f4d-9eb2-417c-8f0c-0e946526dd03 · outbound

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SLORR: Simple and Efficient In-Training Low-Rank Regularization Unresolved cited work

Reference 20

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Observation 030d5334-be21-4b75-8afa-e2fa303ead3a · outbound

This paper cites ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models.

SLORR: Simple and Efficient In-Training Low-Rank Regularization ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 21

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Observation bdd9d4ae-383d-4c3b-94ae-c4d935fcfa44 · outbound

This paper cites DARE: Data Augmented Relation Extraction with GPT-2.

SLORR: Simple and Efficient In-Training Low-Rank Regularization DARE: Data Augmented Relation Extraction with GPT-2

Reference 22

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Observation 55842603-aaad-4a09-a932-7fefaa0edf51 · outbound

This paper cites Trace norm regularization and faster inference for embedded speech recognition RNNs.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Trace norm regularization and faster inference for embedded speech recognition RNNs

Reference 23

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Observation b4e07d4e-f086-43d9-911e-73aa77ef5e25 · outbound

This paper cites Efficient Differentiable Approximation of Generalized Low-rank Regularization.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Efficient Differentiable Approximation of Generalized Low-rank Regularization

Reference 24

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Observation 7abff4e4-fa33-434e-95ff-3169db0ba881 · outbound

This paper cites Low-Rank Prehab: Preparing Neural Networks for SVD Compression, 2025.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Low-Rank Prehab: Preparing Neural Networks for SVD Compression, 2025

Reference 25

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Observation be3eb4ef-fc48-4eca-b8d0-4dfe075aa6de · outbound

This paper cites Compression-aware Training of Neural Networks using Frank-Wolfe.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Compression-aware Training of Neural Networks using Frank-Wolfe

Reference 26

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Observation 63864567-2258-4a62-899a-179bbf76ca3d · outbound

This paper cites Hewa Koneputugodage, Shamane Siriwardhana, Violetta Shevchenko, Karol Pajak, James Snewin, Gil Avra- ham, and Alexander Long.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Hewa Koneputugodage, Shamane Siriwardhana, Violetta Shevchenko, Karol Pajak, James Snewin, Gil Avra- ham, and Alexander Long

Reference 27

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Observation c42a0d91-8464-4886-bcca-d4388b6cd83a · outbound

This paper cites Decoupled Weight Decay Regularization.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Decoupled Weight Decay Regularization

Reference 28

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Observation 00fcfe97-d5a6-46c4-b795-1c29530050b0 · outbound

This paper cites ReLoRA: High-Rank Training Through Low-Rank Updates.

SLORR: Simple and Efficient In-Training Low-Rank Regularization ReLoRA: High-Rank Training Through Low-Rank Updates

Reference 29

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Observation 39e1c134-0093-4bce-a47d-b98a8ceeb8fa · outbound

This paper cites Tenenholtz, Lester Mackey, and Nicolo Fusi.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Tenenholtz, Lester Mackey, and Nicolo Fusi

Reference 30

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Observation 413b52e1-d752-4cb6-a3b4-af752e9a65d3 · outbound

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SLORR: Simple and Efficient In-Training Low-Rank Regularization Unresolved cited work

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Observation fcfe1a4a-e4d6-4c5a-9ac3-9fa19203a98d · outbound

This paper cites Exploring Low Rank Training of Deep Neural Networks.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Exploring Low Rank Training of Deep Neural Networks

Reference 32

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Observation ec937990-5680-48c4-a67b-4b29b6965250 · outbound

This paper cites Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers

Reference 33

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Observation 056692f4-2755-46b6-ba05-bc34b2389fc9 · outbound

This paper cites SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining.

SLORR: Simple and Efficient In-Training Low-Rank Regularization SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining

Reference 34

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Observation bf157eae-3b14-43bd-ab8f-aae6c354c05a · outbound

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SLORR: Simple and Efficient In-Training Low-Rank Regularization Unresolved cited work

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Observation 730d5a5a-15dd-481e-9583-4151a9f46e6d · outbound

This paper cites LOST: Low-rank and Sparse Pre-training for Large Language Models.

SLORR: Simple and Efficient In-Training Low-Rank Regularization LOST: Low-rank and Sparse Pre-training for Large Language Models

Reference 36

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Observation 7869c84b-04b4-415b-8660-b40a9192ca0b · outbound

This paper cites DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures.

SLORR: Simple and Efficient In-Training Low-Rank Regularization DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures

Reference 37

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Observation 69bffd59-31a3-48f5-b6d9-3b4a063308b2 · outbound

This paper cites Perturbation Bounds of Unitary and Subunitary Polar Factors.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Perturbation Bounds of Unitary and Subunitary Polar Factors

Reference 38

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Observation e3a74e11-1a02-45ed-91c2-04eb70a0ac2f · outbound

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SLORR: Simple and Efficient In-Training Low-Rank Regularization Unresolved cited work

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Observation 2f77099c-4aac-487e-90a8-cbba14e03cf7 · outbound

This paper cites Muon: An optimizer for hidden layers in neural networks.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Muon: An optimizer for hidden layers in neural networks

Reference 40

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Observation fc08ee97-e8f0-421a-be16-1aea09339f03 · outbound

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SLORR: Simple and Efficient In-Training Low-Rank Regularization Unresolved cited work

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Observation ce438c5d-39e6-40b1-a3a6-1cda368081cf · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Adam: A Method for Stochastic Optimization

Reference 42

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Observation 941fa3a8-54cf-415d-8322-403b7d0c9cc4 · outbound

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

SLORR: Simple and Efficient In-Training Low-Rank Regularization LLaMA: Open and Efficient Foundation Language Models

Reference 43

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Observation 59699049-e3c3-465e-a0f3-28e0ea3e3ef1 · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

SLORR: Simple and Efficient In-Training Low-Rank Regularization The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 44

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Observation 0cf60711-5546-4cd1-a601-004cfe90fcf7 · outbound

This paper cites Training Compute-Optimal Large Language Models.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Training Compute-Optimal Large Language Models

Reference 45

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Observation 37faf7c3-3e40-4e9a-b8d2-c6b3b08563e4 · outbound

This paper cites The Language Model Evaluation Harness, 07 2024.

SLORR: Simple and Efficient In-Training Low-Rank Regularization The Language Model Evaluation Harness, 07 2024

Reference 46

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Observation 54248ec1-af66-456e-a726-fd8c7f3a80c5 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:06:42.188425Z

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Observation c9c63e2c-0083-44c5-8b47-eab2beef45f3 · outbound

This paper cites URL https:// doi.org/10.18653/v1/p19-1472.

SLORR: Simple and Efficient In-Training Low-Rank Regularization URL https:// doi.org/10.18653/v1/p19-1472

Reference 48

Resolution: verified exact; doi, observed 2026-07-10T02:06:41.870886Z

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Observation ad5d6bdb-2425-48d1-9925-9ba566126ae3 · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

SLORR: Simple and Efficient In-Training Low-Rank Regularization The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 49

Resolution: verified exact; doi, observed 2026-07-10T02:06:41.879232Z

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Observation 25e405ac-0879-4a0b-8a8e-bb250841a86d · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 50

Resolution: verified exact; doi, observed 2026-07-10T02:06:41.881663Z

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Observation 01bc37d2-64a9-4346-8292-098e1600ebd9 · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

SLORR: Simple and Efficient In-Training Low-Rank Regularization PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:06:42.191704Z

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Observation 7c5da7b7-42e3-4b7e-b705-11522c1219ba · outbound

This paper cites Gram Newton-Schulz, 2026.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Gram Newton-Schulz, 2026

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:06:42.427173Z

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Observation db86bd02-c40a-470a-a4be-c821f7d43a52 · outbound

This paper cites Domain Generalization via Nuclear Norm Regularization.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Domain Generalization via Nuclear Norm Regularization

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:06:42.429031Z

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

Observation 8bc0c0f1-e577-41c5-b127-9153a9b57317 · outbound

This paper cites Classics in Applied Mathe- matics, vol.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Classics in Applied Mathe- matics, vol

Reference 54

Resolution: verified exact; doi, observed 2026-07-10T02:06:41.877380Z

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

Observation ea2e056a-a9c9-402d-b710-2525eb8b0cd5 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

SLORR: Simple and Efficient In-Training Low-Rank Regularization PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:06:42.182239Z

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

Observation 9941de5e-142a-49f6-bb01-9b63336cbdfd · outbound

This paper cites PyTorch Image Models, 2019.

SLORR: Simple and Efficient In-Training Low-Rank Regularization PyTorch Image Models, 2019

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:06:42.419867Z

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 1c071790-1d81-4fc2-978d-937d77308673 · outbound

This paper cites TorchVision: PyTorch’s Computer Vision library, November 2016.

SLORR: Simple and Efficient In-Training Low-Rank Regularization TorchVision: PyTorch’s Computer Vision library, November 2016

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:06:42.421662Z

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 f1e7da7b-38d1-42f5-a20d-f96acd52df90 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:06:42.185163Z

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

Observation 353d4477-7104-424f-9d83-ba97f09e929b · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

SLORR: Simple and Efficient In-Training Low-Rank Regularization SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:06:42.417953Z

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 a178c83e-c38f-441c-b43e-3f28b0643cbc · outbound

This paper cites BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression.

SLORR: Simple and Efficient In-Training Low-Rank Regularization BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:06:42.179511Z

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 1980c6bf-aed8-4015-aa21-f2b468c6fa73 · outbound

This paper cites Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T02:06:42.416004Z

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 00347021-8ef1-442d-adcd-c2554085c10b · outbound

This paper cites 2 OLMo 2 Furious.

SLORR: Simple and Efficient In-Training Low-Rank Regularization 2 OLMo 2 Furious

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:06:42.176728Z

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 cbe71c4b-eee5-48dc-8834-83a948dcb766 · outbound

This paper cites an unresolved cited work.

SLORR: Simple and Efficient In-Training Low-Rank Regularization Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-07-10T02:06:42.423593Z

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 ab10fa4e-252d-4a31-9692-48b83003d0a4 · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

SLORR: Simple and Efficient In-Training Low-Rank Regularization WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:06:42.173730Z

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 5f449738-cf4d-45c2-b0f7-d97c3a7efbf3 · outbound

This paper cites rand-m9-mstd0.5-inc1.

SLORR: Simple and Efficient In-Training Low-Rank Regularization rand-m9-mstd0.5-inc1

Reference 65

Resolution: malformed identifier; raw_fallback, observed 2026-07-10T02:06:42.425431Z

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

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