1 citations · 2 across the 5 of their papers we have counts for
6 papers
Towards Green AI: Current status and future research
Christian Clemm, Lutz Stobbe, Kishan Wimalawarne +1
The immense technological progress in artificial intelligence research and applications is increasingly drawing attention to the environmental sustainability of such systems, a fie…
Learning Green's Function Efficiently Using Low-Rank Approximations
Kishan Wimalawarne, Taiji Suzuki, Sophie Langer
Learning the Green's function using deep learning models enables to solve different classes of partial differential equations. A practical limitation of using deep learning for the…
Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs
Kishan Wimalawarne, Taiji Suzuki
We investigate efficient learning from higher-order graph convolution and learning directly from adjacency matrices for node classification. We revisit the scaled graph residual ne…
Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank
Kishan Wimalawarne, Taiji Suzuki
We investigate adaptive layer-wise graph convolution in deep GCN models. We propose AdaGPR to learn generalized Pageranks at each layer of a GCNII network to induce adaptive convol…
Convex Coupled Matrix and Tensor Completion
Kishan Wimalawarne, Makoto Yamada, Hiroshi Mamitsuka
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 com…
Theoretical and Experimental Analyses of Tensor-Based Regression and Classification
Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama
We theoretically and experimentally investigate tensor-based regression and classification. Our focus is regularization with various tensor norms, including the overlapped trace no…