Paper Citation Record · LEDGER

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression

As of 18 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2509.25136.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.25136 v3

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:49:04.152064Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-10T01:58:58.772489Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-10T02:06:42.178030Z

Reference resolution

61 of 61 outbound references displayed

verified exact6
verified fuzzy25
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External citation measurements

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Outbound references

Observation fe2e7ea3-762f-43fc-a53c-2414fc48a8e8 · outbound

This paper cites write newline.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression write newline

Reference 1

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Observation 2fc55f4c-d5fd-4e4c-b162-1c7e880ea7fd · outbound

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Compressing pre-trained language models by matrix decomposition

Reference 2

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Observation f9c131ce-a910-48ab-9d8e-f5cb3bf61bd9 · outbound

This paper cites High Performance Convolutional Neural Networks for Document Processing.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression High Performance Convolutional Neural Networks for Document Processing

Reference 3

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Observation d56d511c-b2b5-4be4-86d4-be0e2f1a0491 · outbound

This paper cites Drone: Data-aware low-rank compression for large nlp models.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Drone: Data-aware low-rank compression for large nlp models

Reference 4

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Observation 8808a307-e82d-48fe-aa3a-d750e668fec3 · outbound

This paper cites Going beyond neural network feature similarity: The network feature complexity and its interpretation using category theory.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Going beyond neural network feature similarity: The network feature complexity and its interpretation using category theory

Reference 5

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Observation 29c6ec3a-7a9f-450f-88b3-6e4b7c7d9b80 · outbound

This paper cites Parseval networks: improving robustness to adversarial examples.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Parseval networks: improving robustness to adversarial examples

Reference 6

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Observation 5bc3edd0-ece9-4684-8b8c-519382e01984 · outbound

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Imagenet: A large-scale hierarchical image database

Reference 7

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Observation cc9d3ded-8af2-4ce1-a80c-205a4175ce7c · outbound

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Exploiting linear structure within convolutional networks for efficient evaluation

Reference 8

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Observation 7181fc49-396d-49c2-8763-ad385ad417ad · outbound

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

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Observation 91335a01-b9ac-47c2-8217-952397b7f242 · outbound

This paper cites Pulp: A linear programming toolkit for python.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Pulp: A linear programming toolkit for python

Reference 10

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Observation 2a061e1b-77d5-47b1-b384-744b0fffea32 · outbound

This paper cites Optimal brain compression: A framework for accurate post-training quantization and pruning.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Optimal brain compression: A framework for accurate post-training quantization and pruning

Reference 11

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Observation 0ccb5981-f0e9-4d16-b86c-cf5068ab7665 · outbound

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression OPTQ : Accurate quantization for generative pre-trained transformers

Reference 12

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Observation 377532e5-f173-4d91-9340-c74db335cf6a · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 13

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Observation 102ab468-a9a8-42ed-a1bf-bdc3137966c3 · outbound

This paper cites Golub and C.F.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Golub and C.F

Reference 14

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Observation 6707cdab-62ae-483a-b94e-8fa093494b9c · outbound

This paper cites Feature variance ratio-guided channel pruning for deep convolutional network acceleration.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Feature variance ratio-guided channel pruning for deep convolutional network acceleration

Reference 15

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Observation 2e1c32e4-afae-4556-bfd4-d211fde01b2b · outbound

This paper cites Deep Residual Learning for Image Recognition.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Deep Residual Learning for Image Recognition

Reference 16

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Observation aa997362-ee34-4eac-a01c-0a7154539700 · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Benchmarking neural network robustness to common corruptions and perturbations

Reference 17

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Observation 228c4d07-9e6e-48c5-bbfb-cae58b28aa43 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Gaussian Error Linear Units (GELUs)

Reference 18

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Observation 7520e77a-b5b8-4892-b780-bc6d7a8ce3d2 · outbound

This paper cites Magma: Enabling exascale performance with accelerated blas and lapack for diverse gpu architectures.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Magma: Enabling exascale performance with accelerated blas and lapack for diverse gpu architectures

Reference 19

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Observation b5e4a6ee-58da-40db-940d-3461cfb311b0 · outbound

This paper cites Sparsity in deep learning: pruning and growth for efficient inference and training in neural networks.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Sparsity in deep learning: pruning and growth for efficient inference and training in neural networks

Reference 20

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Observation c13337cc-0b3d-402a-a0c2-be8a5ed53986 · outbound

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Dynamic low-rank estimation for transformer-based language models

Reference 21

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Observation a1c3e520-aa03-4321-a39f-9f955e479e6d · outbound

This paper cites Carreira-Perpiñán.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Carreira-Perpiñán

Reference 22

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This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 23

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Observation b5071d2e-d931-440d-bfb9-5e74e2010f93 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 24

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Observation 94cb8a45-5d3e-40c5-9c10-eff060f8f333 · outbound

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Speeding up Convolutional Neural Networks with Low Rank Expansions

Reference 25

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This paper cites Stability analysis of fluid flows using Lagrangian Perturbation Theory (LPT): application to the plane Couette flow.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Stability analysis of fluid flows using Lagrangian Perturbation Theory (LPT): application to the plane Couette flow

Reference 26

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Observation 5b016559-9600-4d90-aa69-efa73b3d40be · outbound

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Learning multiple layers of features from tiny images

Reference 27

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This paper cites Imagenet classification with deep convolutional neural networks.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Imagenet classification with deep convolutional neural networks

Reference 28

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Observation a5cd88b4-b3b0-4c1f-8867-fd3af79284d2 · outbound

This paper cites Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

Reference 29

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Deep learning

Reference 30

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This paper cites Training-Free Restoration of Pruned Neural Networks.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Training-Free Restoration of Pruned Neural Networks

Reference 31

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Observation 7eb8b110-2ce3-47a0-970f-e41b101fc45e · outbound

This paper cites Compressing neural networks: Towards determining the optimal layer-wise decomposition.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Compressing neural networks: Towards determining the optimal layer-wise decomposition

Reference 32

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Observation fd78d61a-5d7d-434c-b380-0ce48452ee50 · outbound

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression TorchVision: PyTorch's Computer Vision library , November 2016

Reference 33

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Observation 54221432-d83a-4208-9b08-ca7cb6479cfb · outbound

This paper cites TVSP rune - pruning non-discriminative filters via total variation separability of intermediate representations without fine tuning.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression TVSP rune - pruning non-discriminative filters via total variation separability of intermediate representations without fine tuning

Reference 34

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Unresolved cited work

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This paper cites DFPC : Data flow driven pruning of coupled channels without data.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression DFPC : Data flow driven pruning of coupled channels without data

Reference 36

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Observation 1db16b67-2e87-4cd0-aecc-2f7bf27453b9 · outbound

This paper cites cuSOLVER Library.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression cuSOLVER Library

Reference 37

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression PyTorch: an imperative style, high-performance deep learning library

Reference 38

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This paper cites Stable low-rank tensor decomposition for compression of convolutional neural network.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Stable low-rank tensor decomposition for compression of convolutional neural network

Reference 39

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This paper cites Dobi- SVD : Differentiable SVD for LLM compression and some new perspectives.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Dobi- SVD : Differentiable SVD for LLM compression and some new perspectives

Reference 40

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This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 41

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Observation cdffc05f-97f8-4d48-9fa5-6723ddd603b1 · outbound

This paper cites On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization

Reference 42

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks

Reference 43

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This paper cites Solution methods for the multiple-choice knapsack problem and their applications.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Solution methods for the multiple-choice knapsack problem and their applications

Reference 44

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This paper cites Training data-efficient image transformers &; distillation through attention.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Training data-efficient image transformers &; distillation through attention

Reference 45

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This paper cites Attention is all you need.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Attention is all you need

Reference 46

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This paper cites Forget the data and fine-tuning! just fold the network to compress.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Forget the data and fine-tuning! just fold the network to compress

Reference 47

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Observation 3736fbce-13c2-45da-9620-009cfd283fc2 · outbound

This paper cites Practical Network Acceleration with Tiny Sets.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Practical Network Acceleration with Tiny Sets

Reference 48

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Observation 07db194d-6bc8-4dab-9d37-46b3bb80951b · outbound

This paper cites SVD-LLM V2: Optimizing Singular Value Truncation for Large Language Model Compression.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression SVD-LLM V2: Optimizing Singular Value Truncation for Large Language Model Compression

Reference 49

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Observation 7c4b51f2-c04d-41b2-a923-2df7e9fc6a9b · outbound

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression SVD - LLM : Truncation-aware singular value decomposition for large language model compression

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:49:04.577896Z

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression PyTorch Image Models

Reference 51

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Aggregated Residual Transformations for Deep Neural Networks

Reference 52

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This paper cites GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation

Reference 53

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

Reference 54

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Towards efficient tensor decomposition-based dnn model compression with optimization framework

Reference 55

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search

Reference 56

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

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 57

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This paper cites Dense vision transformer compression with few samples.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Dense vision transformer compression with few samples

Reference 58

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This paper cites @esa (Ref.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression @esa (Ref

Reference 59

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Unresolved cited work

Reference 60

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BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Unresolved cited work

Reference 61

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Pith citing papers

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SLORR: Simple and Efficient In-Training Low-Rank Regularization cites this paper.

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

Reference 60

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