UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models | Paper Citation Record

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UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Unresolved cited work

Reference 1

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This paper cites Evolutionary Optimization of Model Merging Recipes,.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Evolutionary Optimization of Model Merging Recipes,

Reference 2

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This paper cites Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 3

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This paper cites Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

Reference 4

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This paper cites On the Inductive Bias of Neural Tangent Kernels.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models On the Inductive Bias of Neural Tangent Kernels

Reference 5

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This paper cites Food-101 – Mining Discriminative Components with Random Forests.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Food-101 – Mining Discriminative Components with Random Forests

Reference 6

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This paper cites SWAD: Domain Generalization by Seeking Flat Minima.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models SWAD: Domain Generalization by Seeking Flat Minima

Reference 7

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Observation 04f83b44-70dd-4f16-95a1-b6540c5beab4 · outbound

This paper cites Distilling Knowledge via Knowledge Review.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Distilling Knowledge via Knowledge Review

Reference 8

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Observation e9b5e894-49bd-40f7-9b97-bae2e9db8703 · outbound

This paper cites Fusing finetuned models for better pretraining.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Fusing finetuned models for better pretraining

Reference 9

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This paper cites Cimpoi, S.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Cimpoi, S

Reference 10

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

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 12

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This paper cites Agree to Disagree: Adap- tive Ensemble Knowledge Distillation in Gradient Space.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Agree to Disagree: Adap- tive Ensemble Knowledge Distillation in Gradient Space

Reference 13

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This paper cites Learning Factored Representations in a Deep Mixture of Ex- perts.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Learning Factored Representations in a Deep Mixture of Ex- perts

Reference 14

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This paper cites ImageBind: One Embedding Space To Bind Them All.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models ImageBind: One Embedding Space To Bind Them All

Reference 15

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This paper cites Borgwardt, Malte J.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Borgwardt, Malte J

Reference 16

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This paper cites STOCHASTIC WEIGHT A VERAGING IN PARAL- LEL: LARGE-BATCH TRAINING THAT GENERALIZES WELL.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models STOCHASTIC WEIGHT A VERAGING IN PARAL- LEL: LARGE-BATCH TRAINING THAT GENERALIZES WELL

Reference 17

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This paper cites Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation

Reference 18

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This paper cites One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distil- lation.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distil- lation

Reference 19

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This paper cites Deep Residual Learning for Image Recognition.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Deep Residual Learning for Image Recognition

Reference 20

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This paper cites A Comprehensive Overhaul of Feature Distillation.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models A Comprehensive Overhaul of Feature Distillation

Reference 21

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This paper cites Distilling the Knowledge in a Neural Network.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Distilling the Knowledge in a Neural Network

Reference 22

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This paper cites Edit- ing models with task arithmetic.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Edit- ing models with task arithmetic

Reference 23

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This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Averaging Weights Leads to Wider Optima and Better Generalization

Reference 24

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

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 25

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This paper cites Khosla, N.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Khosla, N

Reference 26

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

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models 3D object representations for fine-grained categorization

Reference 27

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

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Learning multiple layers of features from tiny images

Reference 28

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UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Caltech 101, 2022

Reference 29

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This paper cites Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models

Reference 30

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This paper cites Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy

Reference 31

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This paper cites Harmonious atten- tion network for person re-identification.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Harmonious atten- tion network for person re-identification

Reference 32

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This paper cites Implicit Bias of Gradient Descent based Adversarial Training on Separable Data.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Implicit Bias of Gradient Descent based Adversarial Training on Separable Data

Reference 33

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This paper cites MoE-LLaVA: Mixture of Experts for Large Vision-Language Models.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Reference 34

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This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 35

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UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models A ConvNet for the 2020s

Reference 36

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This paper cites Knowledge Amalgamation from Het- erogeneous Networks by Common Feature Learning.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Knowledge Amalgamation from Het- erogeneous Networks by Common Feature Learning

Reference 37

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This paper cites Fine-Grained Visual Classification of Aircraft.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Fine-Grained Visual Classification of Aircraft

Reference 38

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UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Im- proved Knowledge Distillation via Teacher Assistant

Reference 39

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UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models 5, 6, 7, 1

Reference 40

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This paper cites Parkhi, Andrea Vedaldi, Andrew Zisserman, and C.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Parkhi, Andrea Vedaldi, Andrew Zisserman, and C

Reference 41

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This paper cites Correlation Congruence for Knowledge Distillation.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Correlation Congruence for Knowledge Distillation

Reference 42

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This paper cites Automated Flower Classification over a Large Number of Classes.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Automated Flower Classification over a Large Number of Classes

Reference 43

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This paper cites Diverse Weight Averaging for Out-of-Distribution General- ization.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Diverse Weight Averaging for Out-of-Distribution General- ization

Reference 44

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This paper cites Model Ratatouille: Recy- cling Diverse Models for Out-of-Distribution Generalization.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Model Ratatouille: Recy- cling Diverse Models for Out-of-Distribution Generalization

Reference 45

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This paper cites Learning Transferable Visual Models From Natural Language Supervision.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Learning Transferable Visual Models From Natural Language Supervision

Reference 46

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Observation 090944a5-3e84-43b4-abfa-a987ae14835b · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models FitNets: Hints for Thin Deep Nets

Reference 47

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This paper cites UNIC: Universal Classification Models via Multi-teacher Distillation.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models UNIC: Universal Classification Models via Multi-teacher Distillation

Reference 48

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This paper cites Am-radio: Agglomerative vision foundation model reduce all domains into one.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Am-radio: Agglomerative vision foundation model reduce all domains into one

Reference 49

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Observation 95a2ffa3-cd3b-48ab-b1cd-ec8ce6175ac2 · outbound

This paper cites Scaling Vision-Language Models with Sparse Mixture of Experts.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Scaling Vision-Language Models with Sparse Mixture of Experts

Reference 50

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Observation 3cc9b924-ce7b-4d8a-b5a7-78b9b74ca1ba · outbound

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

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 51

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This paper cites RoME: Role-aware Mixture-of-Expert Transformer for Text-to-Video Retrieval.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models RoME: Role-aware Mixture-of-Expert Transformer for Text-to-Video Retrieval

Reference 52

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This paper cites Contrastive Representation Distillation.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Contrastive Representation Distillation

Reference 53

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This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:14.081339Z

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

Observation bc02e6f7-6134-4290-bb13-cb4880443f2a · outbound

This paper cites An Empirical Study of Multimodal Model Merging.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models An Empirical Study of Multimodal Model Merging

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:14.101045Z

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.

Observation 651d385a-b5d9-4260-8c73-701ec9c75e22 · outbound

This paper cites The Implicit Bias for Adaptive Optimization Algorithms on Ho- mogeneous Neural Networks.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models The Implicit Bias for Adaptive Optimization Algorithms on Ho- mogeneous Neural Networks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:14.058375Z

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.

Observation 9f30f923-d7af-416f-93bc-155048b8a973 · outbound

This paper cites Momentum Doesn’t Change The Implicit Bias.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Momentum Doesn’t Change The Implicit Bias

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:14.047356Z

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.

Observation b21c2835-8e94-4227-b198-c8713e2a382b · outbound

This paper cites SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:13.648557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd4d8453-8bcc-4384-9d56-0af2cb1c9762 · outbound

This paper cites an unresolved cited work.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:57:14.069723Z

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.

Observation 8e46fc0b-581e-49ea-9e29-a846414c05ea · outbound

This paper cites SegGPT: Segmenting Everything In Context.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models SegGPT: Segmenting Everything In Context

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:13.656719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19dc4cc7-019f-4a0c-b4ae-d15db6940b6f · outbound

This paper cites an unresolved cited work.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:57:14.035262Z

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.

Observation 303a3c26-8da9-495f-b638-0cd04a23563a · outbound

This paper cites Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:14.023850Z

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.

Observation 7378a023-daa3-4c4f-b3b1-6fd111207434 · outbound

This paper cites Deep Visual Domain Adaptation: A Survey.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Deep Visual Domain Adaptation: A Survey

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:13.652486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dce647e-71fe-4553-b665-b1ffe4eadf8e · outbound

This paper cites Exclusive Supermask Sub- network Training for Continual Learning.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Exclusive Supermask Sub- network Training for Continual Learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:14.011550Z

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.

Observation e2ee8fb6-5cdc-49a4-9c98-648fedde6bbc · outbound

This paper cites TIES-Merging: Resolving Interference When Merging Models.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models TIES-Merging: Resolving Interference When Merging Models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:13.999461Z

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.

Observation 1c2cfbd8-8888-4b30-bb68-5746cf1fa9af · outbound

This paper cites Wide Residual Networks.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Wide Residual Networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:13.987148Z

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.

Observation 9bb880fd-9aa8-4e9c-8958-4149e4a893fa · outbound

This paper cites Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:13.668718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32143dc3-2c81-48d7-a940-c0ed4fa73300 · outbound

This paper cites Hospedales, and Huchuan Lu.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Hospedales, and Huchuan Lu

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:13.964343Z

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.

Observation 29ac1bd4-16ad-40c6-92ee-78b1b5c0a2ef · outbound

This paper cites Decoupled Knowledge Distillation.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Decoupled Knowledge Distillation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:13.953725Z

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.

Observation f3b1d21b-2182-46a7-93ac-44270b978c0a · outbound

This paper cites Task-Oriented Feature Distillation.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Task-Oriented Feature Distillation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:13.976215Z

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.

Observation fb89fa82-6a2f-4db1-bc6c-46925ce51043 · outbound

This paper cites an unresolved cited work.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:57:13.942173Z

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.

Observation fcff9645-ca66-400e-89a3-3c2540cc855e · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:13.463127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd396793-71e4-40df-8928-329bd251c5c9 · outbound

This paper cites an unresolved cited work.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Unresolved cited work

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:13.625852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23a79dff-f774-4ae5-aa7b-90b59c48fb57 · outbound

This paper cites Evolutionary Optimization of Model Merging Recipes.

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models Evolutionary Optimization of Model Merging Recipes

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:13.422698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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