Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models | Paper Citation Record

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This paper cites write newline.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models write newline

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Sharp-maml: Sharpness-aware model-agnostic meta learning

Reference 2

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This paper cites J., and Mandt, S.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models J., and Mandt, S

Reference 3

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This paper cites On the properties of variational approximations of gibbs posteriors.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models On the properties of variational approximations of gibbs posteriors

Reference 4

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This paper cites Sharpness-aware minimization improves language model generalization.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Sharpness-aware minimization improves language model generalization

Reference 5

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Unresolved cited work

Reference 6

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This paper cites and Murthy, K.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Murthy, K

Reference 7

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This paper cites Robust wasserstein profile inference and applications to machine learning.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Robust wasserstein profile inference and applications to machine learning

Reference 8

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This paper cites Weight uncertainty in neural network.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Weight uncertainty in neural network

Reference 9

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This paper cites Improving generalization in federated learning by seeking flat minima.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Improving generalization in federated learning by seeking flat minima

Reference 10

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This paper cites Variational inference with continuously-indexed normalizing flows.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Variational inference with continuously-indexed normalizing flows

Reference 11

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This paper cites Swad: Domain generalization by seeking flat minima.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Swad: Domain generalization by seeking flat minima

Reference 12

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This paper cites Stochastic gradient hamiltonian monte carlo.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Stochastic gradient hamiltonian monte carlo

Reference 13

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This paper cites When vision transformers outperform resnets without pre-training or strong data augmentations.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models When vision transformers outperform resnets without pre-training or strong data augmentations

Reference 14

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This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 15

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

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 16

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

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Imagenet: A large-scale hierarchical image database

Reference 17

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This paper cites Sharp minima can generalize for deep nets.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Sharp minima can generalize for deep nets

Reference 18

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This paper cites G., Shamsi, A., Guo, X.-Y., Mohammadi, A., Alinejad-Rokny, H., Sejdinovic, D., Teney, D., Ranasinghe, D.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models G., Shamsi, A., Guo, X.-Y., Mohammadi, A., Alinejad-Rokny, H., Sejdinovic, D., Teney, D., Ranasinghe, D

Reference 19

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

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 20

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Efficient and scalable bayesian neural nets with rank-1 factors

Reference 21

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This paper cites Kronecker-factored approximate curvature for modern neural network architectures.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Kronecker-factored approximate curvature for modern neural network architectures

Reference 22

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This paper cites Sharpness-aware minimization for efficiently improving generalization.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Sharpness-aware minimization for efficiently improving generalization

Reference 23

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This paper cites Emergent properties of the local geometry of neural loss landscapes.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Emergent properties of the local geometry of neural loss landscapes

Reference 24

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Ghahramani, Z

Reference 25

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Kleywegt, A

Reference 26

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Unresolved cited work

Reference 27

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Structured variational learning of bayesian neural networks with horseshoe priors

Reference 28

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Practical variational inference for neural networks

Reference 29

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Unresolved cited work

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Schmidhuber, J

Reference 31

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Parameter-efficient transfer learning for nlp

Reference 32

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al

Reference 33

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This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 34

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models P., and Wilson, A

Reference 35

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Three Factors Influencing Minima in SGD

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Fantastic generalization measures and where to find them

Reference 37

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P

Reference 38

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Hospedales, T

Reference 39

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Auto-Encoding Variational Bayes

Reference 40

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models P., Salimans, T., and Welling, M

Reference 41

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Determinantal point processes for machine learning

Reference 42

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This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 43

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Observation 33f576a3-d230-4402-9c9b-0853c6e12e51 · outbound

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models The power of scale for parameter-efficient prompt tuning

Reference 44

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Wang, D

Reference 45

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This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 46

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This paper cites P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks

Reference 47

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This paper cites and Welling, M.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Welling, M

Reference 48

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Observation 2b4cfb60-6c3e-49eb-952b-c935c93a0c29 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 49

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This paper cites Variational inference with gaussian score matching.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Variational inference with gaussian score matching

Reference 50

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This paper cites and Khan, M.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Khan, M

Reference 51

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Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Unresolved cited work

Reference 52

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This paper cites Exploring generalization in deep learning.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Exploring generalization in deep learning

Reference 53

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Observation 17eae6f4-ef5d-437c-9ac0-d1a521e3cf0e · outbound

This paper cites Optimal transport model distributional robustness.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Optimal transport model distributional robustness

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.573785Z

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Observation a131fad1-5c57-48d4-81f4-99f7be5e2d66 · outbound

This paper cites Flat seeking bayesian neural networks.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Flat seeking bayesian neural networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.559030Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d43939c-a5eb-46aa-a273-cffc9634e1ab · outbound

This paper cites Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models

Reference 56

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Source-reported events for the cited work

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Observation f3e9cd42-b7fa-424b-bd51-e93c0a1b47a0 · outbound

This paper cites Improving adversarial robustness via promoting ensemble diversity.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Improving adversarial robustness via promoting ensemble diversity

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.546433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 93a4929f-f2fb-405e-b8d9-d7422379b3f6 · outbound

This paper cites Regularizing Neural Networks by Penalizing Confident Output Distributions.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Regularizing Neural Networks by Penalizing Confident Output Distributions

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T05:45:27.986156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77d57ce4-dbda-4f0c-942d-74af8d5359c6 · outbound

This paper cites Relative flatness and generalization.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Relative flatness and generalization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.533156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ef718869-d615-4346-85b0-36c5f49d6b95 · outbound

This paper cites Generalized federated learning via sharpness aware minimization.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Generalized federated learning via sharpness aware minimization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.519787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 70d2bb55-5833-4275-8a59-1a4cb5f91dc5 · outbound

This paper cites Walsh-hadamard variational inference for bayesian deep learning.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Walsh-hadamard variational inference for bayesian deep learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.506316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c842a617-9936-41a2-9774-c02ccf221f7d · outbound

This paper cites L., Bhagavatula, C., and Choi, Y.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models L., Bhagavatula, C., and Choi, Y

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T05:45:28.002428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be80381a-8b49-4ce7-aff4-4f1110a388ab · outbound

This paper cites Certifying some distributional robustness with principled adversarial training.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Certifying some distributional robustness with principled adversarial training

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.482428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4b472ded-0f4f-4152-a8c5-4f1ef26e9400 · outbound

This paper cites Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.469410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 67ea098e-f977-41ba-a2ab-11ff006071f7 · outbound

This paper cites Llama 2: Open foundation and fine-tuned chat models, 2023.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Llama 2: Open foundation and fine-tuned chat models, 2023

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.456399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e9a8a50-54bf-45b6-80e6-3f8e532633ae · outbound

This paper cites Replora: Reparameterizing low-rank adaptation via the perspective of mixture of experts.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Replora: Reparameterizing low-rank adaptation via the perspective of mixture of experts

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.443527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4e1fe64a-e0ce-44af-ace2-41935bc43b76 · outbound

This paper cites Improving generalization with flat hilbert bayesian inference.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Improving generalization with flat hilbert bayesian inference

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.430278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cdcb78fb-e83e-4430-b687-7f28984b1849 · outbound

This paper cites Optimal Transport: Old and New.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Optimal Transport: Old and New

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.417032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7f4ea337-f503-4d8d-a7ff-b3b35250032a · outbound

This paper cites Sharpness-aware gradient matching for domain generalization.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Sharpness-aware gradient matching for domain generalization

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.403742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 39e14942-0499-453f-9355-cf55f1bdf2c0 · outbound

This paper cites BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T05:45:28.033956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8565cd23-af2b-4dbf-bd2e-cb4502d480e1 · outbound

This paper cites The implicit and explicit regularization effects of dropout.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models The implicit and explicit regularization effects of dropout

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.389806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cbf5c28e-8ed5-4027-84d9-1d0dc7a47ef1 · outbound

This paper cites and Teh, Y.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Teh, Y

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.375930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 84dafad9-6f65-4402-8d64-b24839101a6c · outbound

This paper cites and Teh, Y.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models and Teh, Y

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.362130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a3174bd-9c8b-4bf2-979b-8ca6d1718958 · outbound

This paper cites Bayesian Low-rank Adaptation for Large Language Models.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Bayesian Low-rank Adaptation for Large Language Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T05:45:28.049296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d0618a2-f578-430c-b8c5-2e4f7d3f6ada · outbound

This paper cites A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T05:45:28.053309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7de28996-21be-4dd5-bd51-6ba996d8ae93 · outbound

This paper cites Be your own teacher: Improve the performance of convolutional neural networks via self distillation.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Be your own teacher: Improve the performance of convolutional neural networks via self distillation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.347549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9e305e7e-c13d-4059-ace5-e2ff0e3e5e95 · outbound

This paper cites Llama-adapter: Efficient fine-tuning of large language models with zero-initialized attention.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Llama-adapter: Efficient fine-tuning of large language models with zero-initialized attention

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.334369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2fac140b-aead-4835-97ef-3ad2d0c49473 · outbound

This paper cites Bayesian attention belief networks.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Bayesian attention belief networks

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.320749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cf98944f-9de1-441c-bdd1-4d990c265bcd · outbound

This paper cites Flatness-aware minimization for domain generalization.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models Flatness-aware minimization for domain generalization

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.307484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6b6fb689-88a8-4cd5-90f7-98488b02a477 · outbound

This paper cites M., and Lu, H.

Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models M., and Lu, H

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:45:28.292074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Outbound references

Pith citing papers