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pro vyhledávání: '"Resheff, Yehezkel S."'
Deep learning models for image classification have become standard tools in recent years. A well known vulnerability of these models is their susceptibility to adversarial examples. These are generated by slightly altering an image of a certain class
Externí odkaz:
http://arxiv.org/abs/2411.04533
We study whether receiving advice from either a human or algorithmic advisor, accompanied by five types of Local and Global explanation labelings, has an effect on the readiness to adopt, willingness to pay, and trust in a financial AI consultant. We
Externí odkaz:
http://arxiv.org/abs/2101.02555
Publikováno v:
Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1167. Springer, Cham
As AI systems develop in complexity it is becoming increasingly hard to ensure non-discrimination on the basis of protected attributes such as gender, age, and race. Many recent methods have been developed for dealing with this issue as long as the p
Externí odkaz:
http://arxiv.org/abs/1908.02641
Publikováno v:
In Expert Systems With Applications 1 March 2023 213 Part B
Financial data aggregators and Personal Financial Management (PFM) services are software products that help individuals manage personal finances by collecting information from multiple accounts at various Financial Institutes (FIs), presenting data i
Externí odkaz:
http://arxiv.org/abs/1808.00151
Autor:
Resheff, Yehezkel S., Shahar, Moni
Transaction data obtained by Personal Financial Management (PFM) services from financial institutes such as banks and credit card companies contain a description string from which the merchant, and an encoded store identifier may be parsed. However,
Externí odkaz:
http://arxiv.org/abs/1807.05834
Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from
Externí odkaz:
http://arxiv.org/abs/1807.03521
Autor:
Resheff, Yehezkel S., Shahar, Moni
Inferring user characteristics such as demographic attributes is of the utmost importance in many user-centric applications. Demographic data is an enabler of personalization, identity security, and other applications. Despite that, this data is sens
Externí odkaz:
http://arxiv.org/abs/1712.07230
Deep learning has become the method of choice in many application domains of machine learning in recent years, especially for multi-class classification tasks. The most common loss function used in this context is the cross-entropy loss, which reduce
Externí odkaz:
http://arxiv.org/abs/1704.06062
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