Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation

Kavli Affiliate: Jia Liu | First 5 Authors: Haibo Yang, Peiwen Qiu, Prashant Khanduri, Minghong Fang, Jia Liu | Summary: Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been observed that some clients may never participate in FL […]


Continue.. Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation

Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation

Kavli Affiliate: Jia Liu | First 5 Authors: Haibo Yang, Peiwen Qiu, Prashant Khanduri, Minghong Fang, Jia Liu | Summary: Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been observed that some clients may never participate in FL […]


Continue.. Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation

Modulation of aggression by social novelty recognition memory in the hippocampal CA2 region

Kavli Affiliate: Steven Siegelbaum | Authors: Andres Villegas and Steven A Siegelbaum | Summary: The dorsal CA2 subregion (dCA2) of the hippocampus exerts a critical role in social novelty recognition (SNR) memory and in the promotion of social aggression. Whether the social aggression and SNR memory functions of dCA2 are related or represent independent processes […]


Continue.. Modulation of aggression by social novelty recognition memory in the hippocampal CA2 region

Semi-supervised Symmetric Non-negative Matrix Factorization with Low-Rank Tensor Representation

Kavli Affiliate: Ran Wang | First 5 Authors: Yuheng Jia, Jia-Nan Li, Wenhui Wu, Ran Wang, | Summary: Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ability of SNMF. The previous methods introduce the pairwise constraints from the local perspective, i.e., […]


Continue.. Semi-supervised Symmetric Non-negative Matrix Factorization with Low-Rank Tensor Representation

Semi-supervised Symmetric Matrix Factorization with Low-Rank Tensor Representation

Kavli Affiliate: Ran Wang | First 5 Authors: Yuheng Jia, Jia-Nan Li, Wenhui Wu, Ran Wang, | Summary: Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ability of SNMF. The previous methods introduce the pairwise constraints from the local perspective, i.e., […]


Continue.. Semi-supervised Symmetric Matrix Factorization with Low-Rank Tensor Representation