Concept
Variété riemannienne
Publications associées (186)
From low-rank retractions to dynamical low-rank approximation and back
Daniel Kressner, Axel Elie Joseph Séguin, Gianluca Ceruti
In algorithms for solving optimization problems constrained to a smooth manifold, retractions are a well-established tool to ensure that the iterates stay on the manifold. More recently, it has been demonstrated that retractions are a useful concept for ot ...
Springer2024Hausdorff dimension of fermions on a random lattice
Geometric properties of lattice quantum gravity in two dimensions are studied numerically via Monte Carlo on Euclidean Dynamical Triangulations. A new computational method is proposed to simulate gravity coupled with fermions, which allows the study of int ...
Elsevier2024Soft Robot Shape Estimation With IMUs Leveraging PCC Kinematics for Drift Filtering
Josephine Anna Eleanor Hughes, Francesco Stella
The control possibilities for soft robots have long been hindered by the need for reliable methods to estimate their configuration. Inertial measurement units (IMUs) can solve this challenge, but they are affected by well-known drift issues. This letter pr ...
Piscataway2024A structured prediction approach for robot imitation learning
Aude Billard, Iason Batzianoulis, Anqing Duan
We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised le ...
London2023RMAML: Riemannian meta-learning with orthogonality constraints
Meta-learning is the core capability that enables intelligent systems to rapidly generalize their prior ex-perience to learn new tasks. In general, the optimization-based methods formalize the meta-learning as a bi-level optimization problem, that is a nes ...
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