A non-negative factorization approach to node pooling in graph convolutional neural networks
D Bacciu, L Di Sotto - AI* IA 2019–Advances in Artificial Intelligence …, 2019 - Springer
The paper discusses a pooling mechanism to induce subsampling in graph structured data
and introduces it as a component of a graph convolutional neural network. The pooling …
and introduces it as a component of a graph convolutional neural network. The pooling …
Variational bayesian orthogonal nonnegative matrix factorization over the stiefel manifold
Nonnegative matrix factorization (NMF) is one of the best-known multivariate data analysis
techniques. The NMF uniqueness and its rank selection are two major open problems in this …
techniques. The NMF uniqueness and its rank selection are two major open problems in this …
Bayesian Tensor Completion for Network Traffic Data Prediction
Network traffic data prediction plays a significant role in various network applications and is
a fundamental task in network traffic engineering. However, with the development of the …
a fundamental task in network traffic engineering. However, with the development of the …
Bayesian semi-nonnegative matrix tri-factorization to identify pathways associated with cancer phenotypes
Accurate identification of pathways associated with cancer phenotypes (eg, cancer subtypes
and treatment outcomes) could lead to discovering reliable prognostic and/or predictive …
and treatment outcomes) could lead to discovering reliable prognostic and/or predictive …
Bayesian semi-nonnegative tri-matrix factorization to identify pathways associated with cancer types
Identifying altered pathways that are associated with specific cancer types can potentially
bring a significant impact on cancer patient treatment. Accurate identification of such key …
bring a significant impact on cancer patient treatment. Accurate identification of such key …
Bayesian Optimization for Partially Overlapping Covariate Data Sources
D Nguyen - 2022 - aaltodoc.aalto.fi
One problem in the real-world industrial process is how to utilize diverse information on best
practices through different data sources. It becomes more complicated when those best …
practices through different data sources. It becomes more complicated when those best …
Apparatus and method for analysing functional MRI data
M Hutel, S Ourselin - US Patent 11,867,783, 2024 - Google Patents
US11867783B2 - Apparatus and method for analysing functional MRI data - Google Patents
US11867783B2 - Apparatus and method for analysing functional MRI data - Google Patents …
US11867783B2 - Apparatus and method for analysing functional MRI data - Google Patents …
A Novel Recommender System using Hidden Bayesian Probabilistic Model based Collaborative Filtering
X Dai, F Li, X Li, H Liang - 2019 International Joint Conference …, 2019 - ieeexplore.ieee.org
For the problems of data sparseness and cold start of goods in the existing recommendation
algorithm, in this paper, we propose a new method based on the hidden Bayesian method to …
algorithm, in this paper, we propose a new method based on the hidden Bayesian method to …