In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion...
Raquel Urtasun, David J. Fleet, Andreas Geiger, Jo...
This paper presents a novel method for reducing the dimensionality of kernel spaces. Recently, to maintain the convexity of training, loglinear models without mixtures have been u...
The paper addresses the region search problem in three-dimensional (3D) space. The data used is a dynamically growing point cloud as it is typically gathered with a 3D-sensing devi...
Distance-Based Amplitude Panning (DBAP) has recently been proposed as a new technique for panning sound sources in two and three dimensional spaces spaces. In this paper, DBAP is ...
Dimitar Kostadinov, Joshua D. Reiss, Valeri Mladen...
Motion planning for robots with many degrees of freedom requires the exploration of an exponentially large configuration space. Single-query motion planners restrict exploration ...