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Dilated Convolutional Neural Networks for Sequential Manifold-valued Data
Efforts are underway to study ways via which the power of deep neural networks can be extended to non-standard data types such as structured data (e.g., graphs) or manifold-valued data (e.g., unit vectors or special matrices). Often, sizable empirical improvements are possible when the geometry of s...
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| Publicado no: | Proc IEEE Int Conf Comput Vis |
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| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7220031/ https://ncbi.nlm.nih.gov/pubmed/32405275 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/iccv.2019.01072 |
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