نتایج جستجو برای: unlearning

تعداد نتایج: 399  

Journal: :IEEE Transactions on Dependable and Secure Computing 2023

Nowadays, machine learning models, especially neural networks, have became prevalent in many real-world applications. These models are trained based on a one-way trip from user data: as long users contribute their data, there is no way to withdraw. To this end, machine unlearning becomes popular research topic, which allows the model trainer unlearn unexpected data model. In paper, we pr...

Journal: :Sustainability 2022

This paper provides an investigation into how different types of government supports can be used to enhance organizational resilience capacity during the COVID-19 pandemic. Based on resource orchestration theory, this study examines effects direct support and indirect capacity, mediation role digital capability, moderation unlearning. The empirical results from 205 Chinese firms show that have ...

Journal: :Journal of the Royal Society of Medicine 1988

Journal: :Advances in Computational Mathematics 1996

Journal: :Image Vision Comput. 2010
Matej Kristan Danijel Skocaj Ales Leonardis

In this paper we propose a Gaussian-kernel-based online kernel density estimation which can be used for applications of online probability density estimation and online learning. Our approach generates a Gaussian mixture model of the observed data and allows online adaptation from positive examples as well as from the negative examples. The adaptation from the negative examples is realized by a...

2007
MICHAEL TITELBAUM Frank Arntzenius

When Bayesians set out to model rational constraints on the ways agents’ degrees of belief evolve over time, they usually start by stipulating that the agents they are modeling never forget information. But Frank Arntzenius has shown that there can be substantive constraints on the relation between an agent’s degrees of belief at two times even if the agent has forgotten some relevant informati...

2001
Bridget E. Hallam

Halperin's Neuro-Connector model of learning and motivation requires the inclusion of considerable designer knowledge, but also learns continuously, editing and correcting some of this information. This paper describes some experiments with a computer implementation of this biological model which indicate that fast learning of new material is easily acquired as promised. However, diÆculties in ...

Journal: :Transformative Works and Cultures 2008

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