Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks

no code implementations • 20 Sep 2017 • Aditya Gudimella, Ross Story, Matineh Shaker, Ruofan Kong, Matthew Brown, Victor Shnayder, Marcos Campos

Deep reinforcement learning yields great results for a large array of problems, but models are generally retrained anew for each new problem to be solved.

Problem Decomposition reinforcement-learning +1

Manifold unwrapping using density ridges

no code implementations • 6 Apr 2016 • Jonas Nordhaug Myhre, Matineh Shaker, Devrim Kaba, Robert Jenssen, Deniz Erdogmus

Research on manifold learning within a density ridge estimation framework has shown great potential in recent work for both estimation and de-noising of manifolds, building on the intuitive and well-defined notion of principal curves and surfaces.