Forget the Data and Fine-Tuning! Just Fold the Network to Compress | Paper Citation Record

Observation 5830d426-4e57-4ddc-b6b9-f4903a8a5731 · outbound

This paper cites Git Re-Basin: Merging Models modulo Permutation Symmetries.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Git Re-Basin: Merging Models modulo Permutation Symmetries

Reference 1

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Observation f813ff86-a2f7-4978-9cd4-021c77e2156f · outbound

This paper cites Fluctuation-based Adaptive Structured Pruning for Large Language Models.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Fluctuation-based Adaptive Structured Pruning for Large Language Models

Reference 2

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Observation c511df0f-c67d-49a5-9dfd-3d00ab79bef8 · outbound

This paper cites Arduino nano 33 ble documentation.

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Reference 3

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Observation 8fe60b2f-ca4b-4a5f-a851-67bf1dcf51be · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 4

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Observation 75832b77-9ecf-49d8-9024-59f8e4f2936b · outbound

This paper cites k-Means Clustering Is Matrix Factorization.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress k-Means Clustering Is Matrix Factorization

Reference 5

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Observation 4f258fda-1227-4630-908a-630709ab3d8b · outbound

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Reference 6

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Observation 307315ee-30e3-4696-bf92-59af69f1bcdd · outbound

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

Forget the Data and Fine-Tuning! Just Fold the Network to Compress On the Opportunities and Risks of Foundation Models

Reference 7

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Observation 9502dd06-943d-4001-8c9c-468139f86b5a · outbound

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Reference 8

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Observation 396fc62e-d69f-4e10-82d2-31decb9c1025 · outbound

This paper cites Chang, X.

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Reference 9

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Observation 929ce3a0-c442-469a-b2fa-47ea3b379ef1 · outbound

This paper cites Data-Free Learning of Student Networks.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Data-Free Learning of Student Networks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:04:46.434786Z

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Observation bfcd368e-77a0-4551-a918-312721890863 · outbound

This paper cites an unresolved cited work.

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Reference 11

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Observation 53347b84-add1-4584-a5ff-ee18b7aac40d · outbound

This paper cites Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory

Reference 12

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Observation 9dc4b57c-54b9-48e4-a385-7d720e75e0b8 · outbound

This paper cites A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations

Reference 13

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Observation c4c9976d-f03a-445f-bc9c-8caad7729cc9 · outbound

This paper cites Corti, B.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Corti, B

Reference 14

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Observation 671eabdc-8d31-475c-8557-afd95809d041 · outbound

This paper cites REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.506062Z

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Observation d69a7d1c-e0f0-4f0d-842d-e8706d45e406 · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 16

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Observation f3e40d45-48f6-41f8-84f6-40d6b389c303 · outbound

This paper cites Class-dependent Compression of Deep Neural Networks.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Class-dependent Compression of Deep Neural Networks

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:04:46.388333Z

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Observation 4d2fc30f-e89d-4b80-9214-25dd7d9f9e18 · outbound

This paper cites The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks

Reference 18

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Observation 233aa405-3997-4989-92aa-1f595a90414f · outbound

This paper cites Esp-eye development board - espressif systems.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Esp-eye development board - espressif systems

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.517367Z

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Observation d765240e-b12b-4d90-93f7-d0c44ab407bb · outbound

This paper cites Data-Free Adversarial Distillation.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Data-Free Adversarial Distillation

Reference 20

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Observation 4fb39c4d-74c3-422d-b226-173d3a044b51 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 21

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Observation adb2c203-bb89-43cc-ba77-75d0dc6d03b8 · outbound

This paper cites Frantar and D.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Frantar and D

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:04:47.148450Z

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Observation 2584b9bd-59f7-417c-b509-094aea0096fb · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 23

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Observation 99284c22-70a6-4e24-aaa0-5b6928c39c63 · outbound

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

Forget the Data and Fine-Tuning! Just Fold the Network to Compress A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 24

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Observation 572be022-d713-4bfb-a5ad-3a948ad2d211 · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 25

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Observation 81489cd6-2d37-4ea2-a5f9-e91e3cdb26d8 · outbound

This paper cites Implicit Regularization in Matrix Factorization.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Implicit Regularization in Matrix Factorization

Reference 26

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Observation 182ca6f7-9e6a-429a-8227-767a275e80ee · outbound

This paper cites Gupta, A.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Gupta, A

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:04:47.137545Z

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Observation 212a428d-fce3-4225-82e4-f6443efd0bd9 · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 28

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Observation 0353aa0c-e38e-4708-937b-267fd727e84a · outbound

This paper cites Hassibi, D.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Hassibi, D

Reference 29

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Observation a1bc26a3-c372-498b-a23c-094b91e463d4 · outbound

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Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 30

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Observation 1eea47c7-6314-4142-bf6d-e481f5a1ebfb · outbound

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Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:47.108275Z

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Observation 53f6f026-911c-4425-b4de-2ee7404af7f3 · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:47.097674Z

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Observation 5576a2d9-66d2-4181-8490-2d5753b56cf1 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Distilling the Knowledge in a Neural Network

Reference 33

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Observation 74d17b3a-fa00-451d-a0f1-0e6c992e4ce2 · outbound

This paper cites Maestro: Uncovering Low-Rank Structures via Trainable Decomposition.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Maestro: Uncovering Low-Rank Structures via Trainable Decomposition

Reference 34

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Observation 83d9a645-873a-4e78-be6c-345fd995869d · outbound

This paper cites Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

Reference 35

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Observation d6ed70c4-b511-47fb-9be2-b46c0b90a25a · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 36

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Observation 6f540b23-3760-4dbd-b7c6-130d1589649e · outbound

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Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:47.087536Z

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Observation f528b48a-4664-43c4-874e-7f8310ccddea · outbound

This paper cites Dataless Knowledge Fusion by Merging Weights of Language Models.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 38

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Observation e26b8038-8904-4c10-a039-620ef76a52c8 · outbound

This paper cites PopulAtion Parameter Averaging (PAPA).

Forget the Data and Fine-Tuning! Just Fold the Network to Compress PopulAtion Parameter Averaging (PAPA)

Reference 39

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Observation a4d96094-20b1-4b9f-8df0-e02f58942b13 · outbound

This paper cites REPAIR: REnormalizing Permuted Activations for Interpolation Repair.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress REPAIR: REnormalizing Permuted Activations for Interpolation Repair

Reference 40

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Observation d02ab137-d734-4eff-aa19-a3cfd221fc00 · outbound

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Reference 41

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Observation 663a6b4e-9036-4b4b-9637-5433e2720b7d · outbound

This paper cites Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

Reference 42

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Observation d8603e1d-c11c-4593-a210-ad5f54296713 · outbound

This paper cites Krizhevsky, G.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Krizhevsky, G

Reference 43

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Observation 22f021f2-c5c6-43d0-be70-b20f541f601c · outbound

This paper cites Krizhevsky, V.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Krizhevsky, V

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:04:47.057371Z

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Observation b9ed4fd1-0987-4729-b0bd-7e2b7fe681da · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:47.047539Z

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Observation 8025760c-8a37-4d33-a241-5bc9d1a1451b · outbound

This paper cites Kumar, S.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Kumar, S

Reference 46

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Observation 9149a52a-4836-431b-9e4f-d93c28154a1a · outbound

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

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

Reference 47

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Observation 87c7eb06-8168-496a-a4f5-9ab5a9da93e5 · outbound

This paper cites LeCun, J.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress LeCun, J

Reference 48

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Observation 55e69420-7767-4866-ab86-87c03fa4fc83 · outbound

This paper cites Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:04:46.158249Z

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Observation da936086-d311-4845-81c0-cedf217746c0 · outbound

This paper cites Ternary Weight Networks.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Ternary Weight Networks

Reference 50

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Observation d1b96a94-277c-487a-911c-0b93f409b906 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Pruning Filters for Efficient ConvNets

Reference 52

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Observation 4cb9403f-ada6-41ae-b712-85d76b9b4329 · outbound

This paper cites Convergent Learning: Do different neural networks learn the same representations?.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Convergent Learning: Do different neural networks learn the same representations?

Reference 53

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Observation bb16dbef-3cbb-4a46-9369-6dd9548b7ea0 · outbound

This paper cites Lightweight Deep Learning for Resource-Constrained Environments: A Survey.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Lightweight Deep Learning for Resource-Constrained Environments: A Survey

Reference 54

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Observation b9266278-7316-4c8c-a3c4-6d639892e1b3 · outbound

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Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 55

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Observation 92fb97a4-81cf-45f5-bb48-f96d99f26464 · outbound

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Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 56

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Observation a8e475ab-d08b-4203-a58c-b8be45b6ae81 · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 57

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Observation aa524705-3cc4-4f7f-9028-713b5264d042 · outbound

This paper cites Merging Models with Fisher-Weighted Averaging.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Merging Models with Fisher-Weighted Averaging

Reference 58

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Observation e734bb89-f805-4837-b4ab-f79626d263bd · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 59

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Observation e2f5b13e-3097-4204-9b3c-bf428547052c · outbound

This paper cites Pointer Sentinel Mixture Models.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Pointer Sentinel Mixture Models

Reference 60

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Observation c1e9fa88-a3b3-444b-a0a0-b0219e8e561d · outbound

This paper cites Zero-shot Knowledge Transfer via Adversarial Belief Matching.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Zero-shot Knowledge Transfer via Adversarial Belief Matching

Reference 61

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Observation 33bf2f67-7a60-481d-9b48-42c92b2bdaea · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:46.987071Z

Source-reported events for the cited work

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

Observation cba92202-d2cf-4495-8e7c-22e7f5cac1ab · outbound

This paper cites Mordvintsev, C.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Mordvintsev, C

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:04:46.976932Z

Source-reported events for the cited work

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

Observation 3969f1f1-120f-40e7-916e-6179c8bfc661 · outbound

This paper cites Jetson nano - nvidia developer.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Jetson nano - nvidia developer

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:04:46.967091Z

Source-reported events for the cited work

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

Observation 2f43983a-0863-4107-87b9-c6769ff38000 · outbound

This paper cites Papst, D.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Papst, D

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:04:46.956496Z

Source-reported events for the cited work

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

Observation d5bde621-4b57-4d03-a51d-687dd0beeaa8 · outbound

This paper cites Low-Rank Prune-And-Factorize for Language Model Compression.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Low-Rank Prune-And-Factorize for Language Model Compression

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.679663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1eb4dad8-1428-40bd-b7b6-8e3fc56c7b8b · outbound

This paper cites Rombach, A.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Rombach, A

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:04:46.946200Z

Source-reported events for the cited work

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

Observation 714406db-dab9-4dd7-8f33-369ea27abb49 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.685636Z

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Observation 3f00142e-91e5-44ed-a726-e049cc938e2b · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.688656Z

Source-reported events for the cited work

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Observation 09936e55-8cfe-43a8-9340-938f7fb9d73a · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:46.934702Z

Source-reported events for the cited work

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

Observation 3caefbef-7299-4375-96e8-7b0f72ab0653 · outbound

This paper cites Solodskikh, A.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Solodskikh, A

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:04:46.922771Z

Source-reported events for the cited work

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

Observation 652daf9d-6396-473f-975b-92ee447a2c0e · outbound

This paper cites ZipIt! Merging Models from Different Tasks without Training.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress ZipIt! Merging Models from Different Tasks without Training

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.697164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a078906-b549-44ac-8573-c7579dc0eca4 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress A Simple and Effective Pruning Approach for Large Language Models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.700618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a39a95a1-c867-4e98-9b46-8fdb9c56bad5 · outbound

This paper cites Towards Meta-Pruning via Optimal Transport.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Towards Meta-Pruning via Optimal Transport

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:04:46.020543Z

Source-reported events for the cited work

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

Observation 530161e0-0af9-4d53-b9fa-da872f5a5f1e · outbound

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

Forget the Data and Fine-Tuning! Just Fold the Network to Compress LLaMA: Open and Efficient Foundation Language Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.708042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eef904b-9dc7-4eb2-999c-43ac8dad55dd · outbound

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

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.711675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed7f5154-c43c-46cc-b6c0-1d65dad5b737 · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:46.910990Z

Source-reported events for the cited work

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

Observation c02198c1-6c88-4255-84f8-16f680da1c08 · outbound

This paper cites Subspace-Configurable Networks.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Subspace-Configurable Networks

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.718178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce98c7bc-e6f4-4b1d-af6d-2512df5a7fbe · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:46.824182Z

Source-reported events for the cited work

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

Observation d7a66195-8fea-4d7c-8211-b583ea8b3499 · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:46.768334Z

Source-reported events for the cited work

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

Observation a18edc2d-7061-488d-a538-1c4728a36896 · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.728714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c7a72b7-92ed-4287-9219-13ce0e8c5078 · outbound

This paper cites Toward Data Efficient Model Merging between Different Datasets without Performance Degradation.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Toward Data Efficient Model Merging between Different Datasets without Performance Degradation

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:04:45.969081Z

Source-reported events for the cited work

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

Observation 3448c147-45bd-4be3-b39d-6105e7028dd9 · outbound

This paper cites Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.736217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 636cfec5-8ddd-4cbd-b8f6-6af3f0b4cacb · outbound

This paper cites Exploring Structural Sparsity in Neural Image Compression.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Exploring Structural Sparsity in Neural Image Compression

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:04:45.944771Z

Source-reported events for the cited work

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

Observation f83ec558-cad0-48f0-87c1-a76345036b95 · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:04:46.726563Z

Source-reported events for the cited work

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

Observation 118d98d6-06e0-4b5c-b659-543c9f8279e9 · outbound

This paper cites Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.746480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a010022c-d46f-44b7-9bc5-5dabbf73ec8e · outbound

This paper cites @esa (Ref.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress @esa (Ref

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.749827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b304ae6a-4fbd-44cb-87ac-0f27f6108894 · outbound

This paper cites an unresolved cited work.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Unresolved cited work

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.753270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93a28408-840b-4906-9675-03f0ed39f4b5 · outbound

This paper cites hs @ @ Ծ-GĀgz Z(nuxʙK;]lv9qQǔ1g#DΝ.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress hs @ @ Ծ-GĀgz Z(nuxʙK;]lv9qQǔ1g#DΝ

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.756667Z

Source-reported events for the cited work

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Outbound references

Pith citing papers