Jul 30, 2024 · This paper proposes an adaptive geometry search method for Riemannian backbones, which quickly adjusts geometry of data space in backbones to ...
Jul 5, 2024 · This paper proposes an adaptive geometry search method for Riemannian backbones, which quickly adjusts geometry of data space in backbones to ...
The author discusses in some detail the old definitions of the curvature tensors for rigged metrized distributions on manifolds given by Schouten, Wagner, ...
The curvature of a Riemannian manifold can be computed at each point of the curves, while some manifolds have curvatures of a constant value. For example, the ...
Apr 4, 2023 · In this study, we propose Geometry-Adaptive Preconditioned gradient descent (GAP) that can overcome the limitations in MAML.
Missing: Manifolds. | Show results with:Manifolds.
Article "Geometry-adaptive Meta-learning in Riemannian Manifolds" Detailed information of the J-GLOBAL is an information service managed by the Japan ...
Apr 5, 2022 · In this paper, we propose a curvature-adaptive meta-learning method that achieves fast adaptation to manifold data by producing suitable curvature.
In this paper, we introduce RMAML, a meta-learning method that enforces orthogonality constraints to the bi-level optimization problem.
Missing: adaptive | Show results with:adaptive
In this study, we propose Geometry-Adaptive Preconditioned gradient descent (GAP) that can overcome the limitations in MAML; GAP can efficiently meta-learn a ...
Curvature-adaptive meta-learning for fast adaptation to manifold data. Z ... Geometry-adaptive Meta-learning in Riemannian Manifolds. Z Gao. ACM Turing ...