RanDeS: Randomized Delta Superposition for Multi-Model Compression | Paper Citation Record

Observation f1eccf67-0a0f-441c-b157-d22476b60601 · outbound

This paper cites Structured pruning of deep convolutional neural networks.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Structured pruning of deep convolutional neural networks

Reference 1

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Observation 68a065b0-eacf-419f-9593-b71d31d732a4 · outbound

This paper cites Food-101--mining discriminative components with random forests.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Food-101--mining discriminative components with random forests

Reference 2

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Observation 865fb8c8-314e-4d51-8371-0ebde65c7710 · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Remote sensing image scene classification: Benchmark and state of the art

Reference 3

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This paper cites Superposition of many models into one.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Superposition of many models into one

Reference 4

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Observation cc53f51b-eafd-42c0-8790-ec1c6f7c03ba · outbound

This paper cites On the efficacy of knowledge distillation.

RanDeS: Randomized Delta Superposition for Multi-Model Compression On the efficacy of knowledge distillation

Reference 5

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Observation 2bd65762-5858-4592-bd25-2df40dad5dfd · outbound

This paper cites Cimpoi, S.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Cimpoi, S

Reference 6

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Observation 5dd1d952-528c-4eea-a6a5-c7b03b56a4f7 · outbound

This paper cites Deep Learning for Classical Japanese Literature.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Deep Learning for Classical Japanese Literature

Reference 7

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Observation 1b1d199b-02ad-42a6-b79f-4d68f4a42cc5 · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning.

RanDeS: Randomized Delta Superposition for Multi-Model Compression An analysis of single-layer networks in unsupervised feature learning

Reference 8

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Observation 4c64ed37-7cc0-420a-8954-4c3226f79393 · outbound

This paper cites Emnist: Extending mnist to handwritten letters.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Emnist: Extending mnist to handwritten letters

Reference 9

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Observation eb75f7f0-db26-48f9-aa85-b04ea442916b · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.

RanDeS: Randomized Delta Superposition for Multi-Model Compression The mnist database of handwritten digit images for machine learning research

Reference 10

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Observation ee59d966-1dc0-4f8f-8bf9-0c3796679b61 · outbound

This paper cites Depgraph: Towards any structural pruning.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Depgraph: Towards any structural pruning

Reference 11

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Observation 17cb41ca-a7d5-4858-8fcd-f936240453a3 · outbound

This paper cites Linear mode connectivity and the lottery ticket hypothesis.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Linear mode connectivity and the lottery ticket hypothesis

Reference 12

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Observation 96aadcd7-8702-4246-8247-84770a8a3871 · outbound

This paper cites A survey of quantization methods for efficient neural network inference.

RanDeS: Randomized Delta Superposition for Multi-Model Compression A survey of quantization methods for efficient neural network inference

Reference 13

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Observation f37cf13e-1bd7-4cab-bfa1-cb019376562c · outbound

This paper cites Challenges in representation learning: A report on three machine learning contests.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Challenges in representation learning: A report on three machine learning contests

Reference 14

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Observation 20b5f6b6-e189-44af-be49-85fba20a5998 · outbound

This paper cites Knowledge distillation: A survey.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Knowledge distillation: A survey

Reference 15

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Observation 38c44b43-93cd-44fa-b076-345d316a1b38 · outbound

This paper cites Pela: Learning parameter-efficient models with low-rank approximation.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Pela: Learning parameter-efficient models with low-rank approximation

Reference 16

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Observation 60805fab-c6ea-4352-8696-b44704c9e53b · outbound

This paper cites Structured pruning for deep convolutional neural networks: A survey.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Structured pruning for deep convolutional neural networks: A survey

Reference 17

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Observation eaf64051-4b0e-4f1b-b912-a7d705c0eb2a · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 18

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Observation ea6b47d5-6e8e-4646-9bc4-edb7c31749e4 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

RanDeS: Randomized Delta Superposition for Multi-Model Compression LoRA: Low-Rank Adaptation of Large Language Models

Reference 19

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Observation fe23a2c4-d201-4e49-920e-e95c699287e2 · outbound

This paper cites Editing Models with Task Arithmetic.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Editing Models with Task Arithmetic

Reference 20

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Observation b43ba204-37ad-4006-aa08-ba8efe0b428a · outbound

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

RanDeS: Randomized Delta Superposition for Multi-Model Compression Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 21

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Observation 1dafd15c-dde8-4a20-a3af-4305a897309f · outbound

This paper cites 3d object representations for fine-grained categorization.

RanDeS: Randomized Delta Superposition for Multi-Model Compression 3d object representations for fine-grained categorization

Reference 22

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Observation 78e4d469-0acb-4dfb-ac0b-ece59feb5a98 · outbound

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

RanDeS: Randomized Delta Superposition for Multi-Model Compression Learning multiple layers of features from tiny images

Reference 23

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Observation 64da730c-f7ae-4912-8a81-69fc828d2fb7 · outbound

This paper cites Structured compression by weight encryption for unstructured pruning and quantization.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Structured compression by weight encryption for unstructured pruning and quantization

Reference 24

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Observation ce33e99e-f12c-47b3-8911-6b8c0012f999 · outbound

This paper cites Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy

Reference 25

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Observation c7d13a7e-9a92-4398-8fdd-cc26362a7a77 · outbound

This paper cites Losparse: Structured compression of large language models based on low-rank and sparse approximation.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Losparse: Structured compression of large language models based on low-rank and sparse approximation

Reference 26

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Observation 59501d6e-41e1-474c-8809-ba64770045eb · outbound

This paper cites Can unstructured pruning reduce the depth in deep neural networks? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1402--1406, 2023.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Can unstructured pruning reduce the depth in deep neural networks? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1402--1406, 2023

Reference 27

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Observation 5caf369a-4ae3-4476-842b-d8976943cf1f · outbound

This paper cites BitDelta: Your Fine-Tune May Only Be Worth One Bit.

RanDeS: Randomized Delta Superposition for Multi-Model Compression BitDelta: Your Fine-Tune May Only Be Worth One Bit

Reference 28

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Observation 75ecca4d-888e-4f1b-b7f3-8fbadf5a75b7 · outbound

This paper cites Post-training quantization for vision transformer.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Post-training quantization for vision transformer

Reference 29

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Observation 846f1043-126a-4587-b279-86a4d53f95a3 · outbound

This paper cites The flan collection: Designing data and methods for effective instruction tuning.

RanDeS: Randomized Delta Superposition for Multi-Model Compression The flan collection: Designing data and methods for effective instruction tuning

Reference 30

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Observation ca55bd1d-fa27-46c7-b99b-447794dfa0b1 · outbound

This paper cites Merging models with fisher-weighted averaging.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Merging models with fisher-weighted averaging

Reference 31

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Observation 0ac06195-91b3-4d95-8d3e-121747435a9c · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Reading digits in natural images with unsupervised feature learning

Reference 32

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Observation b2ad4a43-a71a-4f76-89b9-6b89cad6b315 · outbound

This paper cites What is being transferred in transfer learning? Advances in neural information processing systems, 33: 0 512--523, 2020.

RanDeS: Randomized Delta Superposition for Multi-Model Compression What is being transferred in transfer learning? Advances in neural information processing systems, 33: 0 512--523, 2020

Reference 33

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Observation 29f6c79f-3b65-4d5a-9803-03ba03393edd · outbound

This paper cites Automated flower classification over a large number of classes.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Automated flower classification over a large number of classes

Reference 34

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Observation 8839bdd1-7e3f-4ac6-90d4-657993fe8213 · outbound

This paper cites Task arithmetic in the tangent space: Improved editing of pre-trained models.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Task arithmetic in the tangent space: Improved editing of pre-trained models

Reference 35

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Observation 6cff0bc7-c120-49d9-92d6-cf18506e88fb · outbound

This paper cites Relational knowledge distillation.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Relational knowledge distillation

Reference 36

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Observation d1ca9fd3-e6cb-4a20-9378-b0484564c4df · outbound

This paper cites Cats and dogs.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Cats and dogs

Reference 37

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Observation 1497a052-df9e-4106-898d-9636f7186730 · outbound

This paper cites Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models

Reference 38

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Observation 7dd9f957-e0f9-4412-810e-4b4c5c4f05e2 · outbound

This paper cites Language models are unsupervised multitask learners.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Language models are unsupervised multitask learners

Reference 39

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Observation 84646c53-29bd-45d0-ae96-ce836f0a672d · outbound

This paper cites Learning transferable visual models from natural language supervision.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Learning transferable visual models from natural language supervision

Reference 40

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Observation 99c104d8-68ee-4858-a504-3114e9f08537 · outbound

This paper cites Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals

Reference 41

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Observation a0085ca6-0303-49fa-905a-fa9148ec5f66 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Recursive deep models for semantic compositionality over a sentiment treebank

Reference 42

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Observation 020f9f46-72f5-4b48-be2a-3ce47b9e24ea · outbound

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RanDeS: Randomized Delta Superposition for Multi-Model Compression Unresolved cited work

Reference 43

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Observation 9ff8ab22-ec49-4f64-9fa0-6a5451475dcc · outbound

This paper cites Fusionbench: A comprehensive benchmark of deep model fusion.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Fusionbench: A comprehensive benchmark of deep model fusion

Reference 44

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Observation 34517050-974c-4721-bc44-73533387c67e · outbound

This paper cites SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models.

RanDeS: Randomized Delta Superposition for Multi-Model Compression SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models

Reference 45

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Observation efb3267d-f4e1-441e-a68b-b9bbf01115a8 · outbound

This paper cites Merging Multi-Task Models via Weight-Ensembling Mixture of Experts.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Merging Multi-Task Models via Weight-Ensembling Mixture of Experts

Reference 46

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Observation eab6bde7-04b5-4208-ad00-4686fa6606d4 · outbound

This paper cites Rotation equivariant cnns for digital pathology.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Rotation equivariant cnns for digital pathology

Reference 47

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Observation 08e07525-72d9-4f3f-a194-611aeefdff20 · outbound

This paper cites Machine Learning Model Sizes and the Parameter Gap.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Machine Learning Model Sizes and the Parameter Gap

Reference 48

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Observation 83d2bd49-9c08-4b1b-a21d-27f5d76c7d3f · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

RanDeS: Randomized Delta Superposition for Multi-Model Compression GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 49

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Observation 05c7037e-8f52-4143-a486-1ccd3ce4c4ca · outbound

This paper cites Localizing Task Information for Improved Model Merging and Compression.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Localizing Task Information for Improved Model Merging and Compression

Reference 50

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Observation 08954015-7cd2-4680-bd0f-cd6a22bb2d63 · outbound

This paper cites Structured Pruning of Large Language Models.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Structured Pruning of Large Language Models

Reference 51

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Observation 1dbd6475-6a97-4d1f-8912-20baad640865 · outbound

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

RanDeS: Randomized Delta Superposition for Multi-Model Compression Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 52

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Observation 75f0d2ea-1abb-4058-8169-f266d22e8c96 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 53

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Observation 59c3436e-080d-4364-b90d-c0162468a59a · outbound

This paper cites Ehinger, Aude Oliva, and Antonio Torralba.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Ehinger, Aude Oliva, and Antonio Torralba

Reference 54

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Observation a657afef-66c5-4034-99c9-2914ad6ff182 · outbound

This paper cites Ties-merging: Resolving interference when merging models.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Ties-merging: Resolving interference when merging models

Reference 55

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Observation 3562587e-a1fc-41bc-86c8-bc080ceab0f7 · outbound

This paper cites AdaMerging: Adaptive Model Merging for Multi-Task Learning.

RanDeS: Randomized Delta Superposition for Multi-Model Compression AdaMerging: Adaptive Model Merging for Multi-Task Learning

Reference 56

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Observation 204ad4d4-3248-4de1-88f4-3714a506b854 · outbound

This paper cites Language models are super mario: Absorbing abilities from homologous models as a free lunch.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Language models are super mario: Absorbing abilities from homologous models as a free lunch

Reference 57

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Observation b6ed2f2a-11f6-49c5-a1e1-52251f0b0b9a · outbound

This paper cites On compressing deep models by low rank and sparse decomposition.

RanDeS: Randomized Delta Superposition for Multi-Model Compression On compressing deep models by low rank and sparse decomposition

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:49.875016Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f644a6da-e966-4943-be86-b902d55e2619 · outbound

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

RanDeS: Randomized Delta Superposition for Multi-Model Compression Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:49.856296Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2ac57300-169f-4989-8d62-7e0c8ec6e504 · outbound

This paper cites Decoupled knowledge distillation.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Decoupled knowledge distillation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:49.841376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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