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A structured prediction approach for robot imitation learning

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Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Nicolas Henri Bernard Flammarion

We consider minimizing a nonconvex, smooth function

f

on a Riemannian manifold

\mathcal{M}

. We show that a perturbed version of Riemannian gradient descent algorithm converges to a second-order stationary point (and hence is able to escape saddle point ...

2019

A structured prediction approach for robot imitation learning

Graph Chatbot

Chattez avec Graph Search

A Gauss-Bonnet Theorem for Asymptotically Conical Manifolds and Manifolds with Conical Singularities

Renormalization of quantum field theory on Riemannian manifolds

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Some regularity results for p-harmonic mappings between Riemannian manifolds

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Unsupervised machine learning in atomistic simulations, between predictions and understanding

An Optimal Planning Framework to Deploy Self-Reconfigurable Modular Robots

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Geometric deep learning: going beyond Euclidean data

Detecting Anisotropic Inclusions Through EIT

Renormalization of quantum field theory on Riemannian manifolds

A Gauss-Bonnet Theorem for Asymptotically Conical Manifolds and Manifolds with Conical Singularities

Some regularity results for p-harmonic mappings between Riemannian manifolds

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

An Optimal Planning Framework to Deploy Self-Reconfigurable Modular Robots

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Detecting Anisotropic Inclusions Through EIT

Geometric deep learning: going beyond Euclidean data