3D Segmentation Learning From Sparse Annotations and Hierarchical Descriptors
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2021.3088796
Abstract: One of the main obstacles to 3D semantic segmentation is the significant amount of endeavor required to generate expensive point-wise annotations for fully supervised training. To alleviate manual efforts, we propose GIDSeg, a novel approach… read more here.
Keywords: sparse annotations; annotations hierarchical; learning sparse; segmentation ... See more keywords
Learning Sparse Graphs for Prediction of Multivariate Data Processes
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2019.2896435
Abstract: We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-free and computationally efficient manner. Specifically, the… read more here.
Keywords: multivariate data; graphs; prediction multivariate; learning sparse ... See more keywords
Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signals
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3165468
Abstract: In this letter, we propose an algorithm for learning a sparse weighted graph by estimating its adjacency matrix under the assumption that the observed signals vary smoothly over the nodes of the graph. The proposed… read more here.
Keywords: learning sparse; sparse graphs; majorization minimization; proposed algorithm ... See more keywords
Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics
Sign Up to like & getrecommendations! Published in 2022 at "Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing"
DOI: 10.1142/9789811270611_0044
Abstract: Federated learning is becoming increasingly more popular as the concern of privacy breaches rises across disciplines including the biological and biomedical fields. The main idea is to train models locally on each server using data… read more here.
Keywords: bayesian models; federated learning; models applications; learning sparse ... See more keywords