Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Suri Yaddanapudi"'
Autor:
Aashiq Muhamed, Liang Li, Xingjian Shi, Suri Yaddanapudi, Wayne Chi, Dylan Jackson, Rahul Suresh, Zachary C. Lipton, Alex J. Smola
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:408-417
Autoregressive models using Transformers have emerged as the dominant approach for music generation with the goal of synthesizing minute-long compositions that exhibit large-scale musical structure. These models are commonly trained by minimizing the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be0c8ec7ba0d90ab81d6a1f237844e80
https://doi.org/10.1609/aaai.v35i1.16117
https://doi.org/10.1609/aaai.v35i1.16117
Autor:
Yunzhe Tao, Tao Sun, Aashiq Muhamed, Sahika Genc, Dylan Jackson, Ali Arsanjani, Suri Yaddanapudi, Liang Li, Prachi Kumar
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021
In this work, we propose to use a deep learning framework for decoding the electroencephalogram (EEG) signals of human brain activities. More specifically, we learn an end-to-end model that recognizes natural images or motor imagery by the EEG data t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12cdf7f5324e9de2327c1a4a792ee201
https://pubmed.ncbi.nlm.nih.gov/34891254
https://pubmed.ncbi.nlm.nih.gov/34891254
We describe a novel approach for generating music using a self-correcting, non-chronological, autoregressive model. We represent music as a sequence of edit events, each of which denotes either the addition or removal of a note---even a note previous
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8838c8c9e599569ecb4af68fadde611e
