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pro vyhledávání: '"Figueiredo, Flávio"'
Music digitalization has introduced new forms of composition known as "musical borrowings", where composers use elements of existing songs -- such as melodies, lyrics, or beats -- to create new songs. Using Who Sampled data and Google Trends, we exam
Externí odkaz:
http://arxiv.org/abs/2411.01242
With over a billion active users, TikTok's video-sharing service is currently one of the largest social media websites. This rise in TikTok's popularity has made the website a central platform for music discovery. In this paper, we analyze how TikTok
Externí odkaz:
http://arxiv.org/abs/2411.01239
Analyzing musical influence networks, such as those formed by artist influence or sampling, has provided valuable insights into contemporary Western music. Here, computational methods like centrality rankings help identify influential artists. Howeve
Externí odkaz:
http://arxiv.org/abs/2410.15996
In recent years, deep learning has achieved formidable results in creative computing. When it comes to music, one viable model for music generation are Transformer based models. However, while transformers models are popular for music generation, the
Externí odkaz:
http://arxiv.org/abs/2410.10515
Autor:
Resende, Guilherme H., Nery, Luiz F., Benevenuto, Fabrício, Zannettou, Savvas, Figueiredo, Flavio
Language is a dynamic aspect of our culture that changes when expressed in different technologies/communities. Online social networks have enabled the diffusion and evolution of different dialects, including African American English (AAE). However, t
Externí odkaz:
http://arxiv.org/abs/2401.12720
Copulas are powerful statistical tools for capturing dependencies across data dimensions. Applying Copulas involves estimating independent marginals, a straightforward task, followed by the much more challenging task of determining a single copulatin
Externí odkaz:
http://arxiv.org/abs/2309.16391
Autor:
Giori, Felipe, Figueiredo, Flavio
Pairwise Causal Discovery is the task of determining causal, anticausal, confounded or independence relationships from pairs of variables. Over the last few years, this challenging task has promoted not only the discovery of novel machine learning mo
Externí odkaz:
http://arxiv.org/abs/2212.01279
Autor:
Trevisan, Martino, Vassio, Luca, Drago, Idilio, Mellia, Marco, Murai, Fabricio, Figueiredo, Flavio, da Silva, Ana Paula Couto, Almeida, Jussara M.
Publikováno v:
HT19: Proceedings of the 30th ACM Conference on Hypertext and Social Media. September 2019. Pages 247-251. Association for Computing Machinery
Online Social Networks (OSNs) allow personalities and companies to communicate directly with the public, bypassing filters of traditional medias. As people rely on OSNs to stay up-to-date, the political debate has moved online too. We witness the sud
Externí odkaz:
http://arxiv.org/abs/1904.11719
Akademický článek
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We here focus on the task of learning Granger causality matrices for multivariate point processes. In order to accomplish this task, our work is the first to explore the use of Wold processes. By doing so, we are able to develop asymptotically fast M
Externí odkaz:
http://arxiv.org/abs/1807.04595