The characteristic methods are known to be very efficient for convection-diffusion problems including the Navier-Stokes equations. Convergence is established when the integrals ar...
In this paper, we propose a novel adaptive step-size approach for policy gradient reinforcement learning. A new metric is defined for policy gradients that measures the effect of ...
Takamitsu Matsubara, Tetsuro Morimura, Jun Morimot...
Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework i...
Dynamic voltage and frequency scaling (DVFS) is a wellknown technique for gaining energy savings on desktop and laptop computers. However, its use in server settings requires care...
Shuyi Chen, Kaustubh R. Joshi, Matti A. Hiltunen, ...
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
In this paper we present a technique for computing translational gradients of indirect surface reflectance in scenes containing participating media and significant occlusions. The...
Wojciech Jarosz, Matthias Zwicker, Henrik Wann Jen...
The initial activity-independent formation of a topographic map in the retinotectal system has long been thought to rely on the matching of molecular cues expressed in gradients i...
Several business applications such as marketing basket analysis, clickstream analysis, fraud detection and churning migration analysis demand gradient data analysis. By employing g...
Image gradients form powerful cues in a host of vision and graphics applications. In this paper, we consider multiple views of a textured planar scene and consider the problem of ...