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Found 8 papers, 2 papers with code
Learning Green's Function Efficiently Using Low-Rank Approximations
Learning the Green's function using deep learning models enables to solve different classes of partial differential equations.
Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs
Additionally, we propose adaptive learning between directly graph polynomial convolution models and learning directly from the adjacency matrix.
Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank
We investigate adaptive layer-wise graph convolution in deep GCN models.
Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms
Coupled norms have emerged as a convex method to solve coupled tensor completion.
Convex Coupled Matrix and Tensor Completion
We propose a set of convex low rank inducing norms for a coupled matrices and tensors (hereafter coupled tensors), which shares information between matrices and tensors through common modes.
Theoretical and Experimental Analyses of Tensor-Based Regression and Classification
We theoretically and experimentally investigate tensor-based regression and classification.
Convex Factorization Machine for Regression
We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs).
Multitask learning meets tensor factorization: task imputation via convex optimization
We study a multitask learning problem in which each task is parametrized by a weight vector and indexed by a pair of indices, which can be e. g, (consumer, time).
