RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability | Paper Citation Record

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability C.; Soatto, S.; and Perona, P

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Estimating Example Difficulty Using Variance of Gradients

Reference 4

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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This paper cites Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models.

RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Invariant Risk Minimization

Reference 7

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

Reference 12

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

Reference 14

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability S.; Smith, N

Reference 15

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Adversarial Examples Are a Natural Consequence of Test Error in Noise

Reference 17

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability F.; Riley, G.; and Grahn, H

Reference 18

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Generalisation in humans and deep neural networks

Reference 19

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Test-Time Adaptation via Conjugate Pseudo-labels

Reference 21

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

Reference 23

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 25

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 26

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

Reference 27

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Quantifying the Carbon Emissions of Machine Learning

Reference 31

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Z.; Astrid, M.; and Lee, S.-I

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability RandoMix: A mixed sample data augmentation method with multiple mixed modes

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Y.; et al

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Efficient Test-Time Model Adaptation without Forgetting

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Carbon Emissions and Large Neural Network Training

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability M.; and Embrechts, M

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Green AI

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Very Deep Convolutional Networks for Large-Scale Image Recognition

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Energy and Policy Considerations for Deep Learning in NLP

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability A Systematic Review of Green AI

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Continual Test-Time Domain Adaptation

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Unresolved cited work

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 731168dc-3d30-4aae-ba50-114d898a74c0 · outbound

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

RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T11:26:19.645540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7633d3e0-3b68-436a-a71d-d57c41e2eb55 · outbound

This paper cites Energy Efficiency of Training Neural Network Architectures: An Empirical Study.

RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Energy Efficiency of Training Neural Network Architectures: An Empirical Study

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T11:26:19.650444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 560561bd-613c-4173-be0f-ecb97a98f41d · outbound

This paper cites A Fourier Perspective on Model Robustness in Computer Vision.

RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability A Fourier Perspective on Model Robustness in Computer Vision

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T11:26:19.655658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9863496a-7c90-4699-8e3c-1e478a3c4a0c · outbound

This paper cites Are All Layers Created Equal?.

RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Are All Layers Created Equal?

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T11:26:19.660550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7378ce58-d9d1-4db1-916f-38aef8c9a468 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability mixup: Beyond Empirical Risk Minimization

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T11:26:19.665551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers