Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Berivan Isik"'
Publikováno v:
Frontiers in Signal Processing, Vol 2 (2022)
We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the parameters of the volumetric function. We model the volumetric functi
Externí odkaz:
https://doaj.org/article/5d8aeb5782674fd9a48a32a322c46ba0
Autor:
Berivan Isik, Tsachy Weissman
Publikováno v:
2022 IEEE International Symposium on Information Theory (ISIT).
Autor:
Berivan Isik, Tsachy Weissman
Publikováno v:
IEEE Journal on Selected Areas in Information Theory. :1-1
Storage-efficient privacy-preserving learning is crucial due to increasing amounts of sensitive user data required for modern learning tasks. We propose a framework for reducing the storage cost of user data while at the same time providing privacy g
Autor:
Berivan Isik, Kristy Choi, Xin Zheng, Tsachy Weissman, Stefano Ermon, H.-S. Philip Wong, Armin Alaghi
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices. Despite the significant progress in NN model compression, there has been considerably less investigation in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2c4f0e300b677edc7d25eb2ef19f454
http://arxiv.org/abs/2102.07725
http://arxiv.org/abs/2102.07725
The large communication cost for exchanging gradients between different nodes significantly limits the scalability of distributed training for large-scale learning models. Motivated by this observation, there has been significant recent interest in t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b779096f8c8d7436e5b1f2d34631ac2