We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings (LLE) to unseen data. We also present a principled ...
S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri ...
We consider standard algorithms of finite graph theory, like for instance shortest path algorithms. We present two general methods to polynomially extend these algorithms to infi...
Abstract. A novel adaptive and exemplar-based approach is proposed for image restoration (denoising) and representation. The method is based on a pointwise selection of similar ima...
In classical training methods for node open fault, we need to consider many potential faulty networks. When the multinode fault situation is considered, the space of potential faul...
We present a remeshed vortex particle method for incompressible flow simulations on GPUs. The particles are convected in a Lagrangian frame and are periodically reinitialized on a ...