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Found 6 papers, 0 papers with code
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients.
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data
This paper considers a type of incremental aggregated gradient (IAG) method for large-scale distributed optimization.
Automatic tagging of knowledge points for K12 math problems
In this paper, K12 math problems taken as the research object, the LABS model based on label-semantic attention and multi-label smoothing combining textual features is proposed to improve the automatic tagging of knowledge points for math problems.
Exact Community Recovery over Signed Graphs
Signed graphs encode similarity and dissimilarity relationships among different entities with positive and negative edges.
Distributionally Robust Graph Learning from Smooth Signals under Moment Uncertainty
To address this issue, we propose a novel graph learning model based on the distributionally robust optimization methodology, which aims to identify a graph that not only provides a smooth representation of but is also robust against uncertainties in the observed signals.
Interactive Visual Exploration of Topic Models using Graphs
Probabilistic topic modeling is a popular and powerful family of tools for uncovering thematic structure in large sets of unstructured text documents.
