Rethinking Evaluation Methods for Machine Unlearning
Rethinking the Evaluation of Out-of-Distribution ... UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models ...
Model Sparsity Can Simplify Machine Unlearning ... We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of ...
Machine unlearning (MU) has emerged as a promising solution, capable of removing undesired generative capabilities from DMs. However, existing MU evaluation ...
The rise of Large Language Models: from fundamentals to application
Machine unlearning: A set of techniques to selectively remove or "unlearn ... How do Humans Evaluate Machine. Translation. https://aclanthology.org/W15 ...
ContentList - Sage Research Methods
What is Machine Learning? https://methods.sagepub.com/video/what-is-machine ... Evaluation as an Instrument of Development Policy, https://methods ...
Full article: Beliefs about child witnesses: A survey of professionals
After evaluating suggestive therapeutic techniques, researchers were able to inform the legal system about possible erroneous effects on memory (Ceci and ...
Rethinking Adversarial Robustness in the Context of the Right to be ...
While existing machine unlearning methods prioritize the protection of individuals' private data, they overlook investigating the unlearned ...
Rethinking Pruning Large Language Models: Benefits and Pitfalls of ... What do large language models need for machine translation evaluation?
Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation ... As some recent information security legislation endowed users with unconditional ...
Rethinking the Evaluation of Neural Machine Translation
In this paper, we propose a novel evaluation protocol, which not only avoids the effect of search errors but provides a system-level evaluation in the ...
Unlearning Backdoor Attacks through Gradient-Based Model Pruning
ensuring the integrity and reliability of machine learning models. However ... Following each pruning round, we evaluate the unlearning loss and main.
Scientific Program | MIDL 2024
To overcome this limitation, we propose a novel semi-supervised segmentation method that leverages mostly unlabeled images and a small set of labeled images in ...
A nonlinear macroscopic multi-phasic model for describing ...
The model shows that the transport coupling between ions and ionic solution exists only when the porous material has a membrane-like feature, ...
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A COGNITIVE ENTRENCHMENT PERSPECTIVE - jstor
In this article I review and reconsider re? search on the inflexibility-related limitations of domain expertise by introducing the concept of cognifive ...
(PDF) Rethinking Leadership | Kurt April and Robert Macdonald
... machine models, we begin to appreciate our wholeness. Leaders need to begin ... unlearning obsolete practices and behaviours, mindsets, and skill-sets.
If there is any way to undo the unlearning, there is also a way to use that method to identify the weights carrying the information to stop them from conveying ...
Evaluation Metrics for Machine Unlearning[v1] - Preprints.org
Unlearning effectiveness examines the degree to which data is removed from the model, utilizing methods such as data removal completeness, ...
This phenomenon calls for a new paradigm, namely machine unlearning, to make ML models forget about particular data. It turns out that recent works on machine ...