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of 85
pro vyhledávání: '"Liem, Cynthia"'
As audio machine learning outcomes are deployed in societally impactful applications, it is important to have a sense of the quality and origins of the data used. Noticing that being explicit about this sense is not trivially rewarded in academic pub
Externí odkaz:
http://arxiv.org/abs/2410.03676
Autor:
Daniil, Savvina, Slokom, Manel, Cuper, Mirjam, Liem, Cynthia C. S., van Ossenbruggen, Jacco, Hollink, Laura
Statements on the propagation of bias by recommender systems are often hard to verify or falsify. Research on bias tends to draw from a small pool of publicly available datasets and is therefore bound by their specific properties. Additionally, imple
Externí odkaz:
http://arxiv.org/abs/2409.08046
Most music widely consumed in Western Countries contains song lyrics, with U.S. samples reporting almost all of their song libraries contain lyrics. In parallel, social science theory suggests that personal values - the abstract goals that guide our
Externí odkaz:
http://arxiv.org/abs/2408.12694
Introduction to the Special Issue on Advanced Technologies in Assessment: A Science-Practice Concern
Autor:
König, Cornelius, Demetriou, Andrew, Glock, Philipp, Hiemstra, Annemarie, Iliescu, Dragos, Ionescu, Camelia, Langer, Markus, Liem, Cynthia, Linnenbürger, Anja, Siegel, Rudolf, Vartholomaios, Illias
Publikováno v:
Personnel Assessment and Decisions, Vol 6, Iss 1 (2020)
This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous oppo
Externí odkaz:
https://doaj.org/article/18f052c3b1414b24a5d52930373e8982
Developments in the field of Artificial Intelligence (AI), and particularly large language models (LLMs), have created a 'perfect storm' for observing 'sparks' of Artificial General Intelligence (AGI) that are spurious. Like simpler models, LLMs dist
Externí odkaz:
http://arxiv.org/abs/2402.03962
Counterfactual explanations offer an intuitive and straightforward way to explain black-box models and offer algorithmic recourse to individuals. To address the need for plausible explanations, existing work has primarily relied on surrogate models t
Externí odkaz:
http://arxiv.org/abs/2312.10648
The equitable distribution of academic data is crucial for ensuring equal research opportunities, and ultimately further progress. Yet, due to the complexity of using the API for audio data that corresponds to the Million Song Dataset along with its
Externí odkaz:
http://arxiv.org/abs/2308.16389
Autor:
Altmeyer, Patrick, Angela, Giovan, Buszydlik, Aleksander, Dobiczek, Karol, van Deursen, Arie, Liem, Cynthia C. S.
Publikováno v:
in 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Raleigh, NC, USA, 2023 pp. 418-431
Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the goal is to find valid counterfactuals for an individual instance that f
Externí odkaz:
http://arxiv.org/abs/2308.08187
Publikováno v:
JuliaCon Proceedings, 1(1), 130 (2023)
We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inputs into a model need to change to yield specific model predictions. Ex
Externí odkaz:
http://arxiv.org/abs/2308.07198
The present work is part of a research line seeking to uncover the mysteries of what lies behind people's musical preferences in order to provide better music recommendations. More specifically, it takes the angle of personal values. Personal values
Externí odkaz:
http://arxiv.org/abs/2302.10088