185 citations · 945 across the 83 of their papers we have counts for
24 papers · 1 filter
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…
Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction
Taiji Suzuki, Denny Wu, Atsushi Nitanda
The mean-field Langevin dynamics (MFLD) is a nonlinear generalization of the Langevin dynamics that incorporates a distribution-dependent drift, and it naturally arises from the op…
Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons
Wei Huang, Yuan Cao, Haonan Wang +2
Graph neural networks (GNNs) have demonstrated remarkable capabilities in learning from graph-structured data, often outperforming traditional Multilayer Perceptrons (MLPs) in nume…
Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
Shokichi Takakura, Taiji Suzuki
Despite the great success of Transformer networks in various applications such as natural language processing and computer vision, their theoretical aspects are not well understood…
Koopman-based generalization bound: New aspect for full-rank weights
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa +2
We propose a new bound for generalization of neural networks using Koopman operators. Whereas most of existing works focus on low-rank weight matrices, we focus on full-rank weight…
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
Tomoya Murata, Taiji Suzuki
Differential private optimization for nonconvex smooth objective is considered. In the previous work, the best known utility bound is $\widetilde O(\sqrt{d}/(n\varepsilon_\mathrm{D…