Zobrazeno 1 - 10
of 211
pro vyhledávání: '"Foster, Jack"'
Adversarial attacks by malicious actors on machine learning systems, such as introducing poison triggers into training datasets, pose significant risks. The challenge in resolving such an attack arises in practice when only a subset of the poisoned d
Externí odkaz:
http://arxiv.org/abs/2406.09173
We present a machine unlearning approach that is both retraining- and label-free. Most existing machine unlearning approaches require a model to be fine-tuned to remove information while preserving performance. This is computationally expensive and n
Externí odkaz:
http://arxiv.org/abs/2402.19308
Data entry constitutes a fundamental component of the machine learning pipeline, yet it frequently results in the introduction of labelling errors. When a model has been trained on a dataset containing such errors its performance is reduced. This lea
Externí odkaz:
http://arxiv.org/abs/2402.10098
To comply with AI and data regulations, the need to forget private or copyrighted information from trained machine learning models is increasingly important. The key challenge in unlearning is forgetting the necessary data in a timely manner, while p
Externí odkaz:
http://arxiv.org/abs/2402.01401
Autor:
Foster, Jack, Brintrup, Alexandra
The pursuit of long-term autonomy mandates that machine learning models must continuously adapt to their changing environments and learn to solve new tasks. Continual learning seeks to overcome the challenge of catastrophic forgetting, where learning
Externí odkaz:
http://arxiv.org/abs/2309.08546
Machine unlearning, the ability for a machine learning model to forget, is becoming increasingly important to comply with data privacy regulations, as well as to remove harmful, manipulated, or outdated information. The key challenge lies in forgetti
Externí odkaz:
http://arxiv.org/abs/2308.07707
Organisations often struggle to identify the causes of change in metrics such as product quality and delivery duration. This task becomes increasingly challenging when the cause lies outside of company borders in multi-echelon supply chains that are
Externí odkaz:
http://arxiv.org/abs/2307.12157
Autor:
Darragh-Ford, Elise, Mantz, Adam B., Rasia, Elena, Allen, Steven W., Morris, R. Glenn, Foster, Jack, Schmidt, Robert W., Wenrich, Guillermo
The relationship linking a galaxy cluster's total mass with the concentration of its mass profile and its redshift is a fundamental prediction of the Cold Dark Matter (CDM) paradigm of cosmic structure formation. However, confronting those prediction
Externí odkaz:
http://arxiv.org/abs/2302.10931