4 accepted papers
Point-based models have achieved impressive success in point cloud processing, but current methods mainly address issues such as disorder, sparsity, and rotation of point clouds. In addition to these characteristics, we also found that for the point set P (X<inf xmlns:mml="http://www.w3.org/1998/Mat…
A point cloud is a geometric data structure that is a set of coordinates p(x, y, z) of points, with additional normal n(n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> , n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http:/…
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and frame-level classification accuracy, kernel-based acoustic models are as effective as their DNN counterparts. However, on t…
A good measure of similarity between data points is crucial to many tasks in machine learning. Similarity and metric learning methods learn such measures automatically from data, but they do not scale well respect to the dimensionality of the data. In this paper, we propose a method that can learn…