activity
20082021
most citedTree-Independent Dual-Tree Algorithms

7 citations · 18 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD2021

I-vector Based Within Speaker Voice Quality Identification on connected speech

Chuyao Feng, Eva van Leer, Mackenzie Lee Curtis +1

Voice disorders affect a large portion of the population, especially heavy voice users such as teachers or call-center workers. Most voice disorders can be treated effectively with…

eess.AS2020

A Multi-Channel Temporal Attention Convolutional Neural Network Model for Environmental Sound Classification

You Wang, Chuyao Feng, David V. Anderson

Recently, many attention-based deep neural networks have emerged and achieved state-of-the-art performance in environmental sound classification. The essence of attention mechanism…

cs.CV2015★ 7 cited

Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context

S. Hussain Raza, Ahmad Humayun, Matthias Grundmann +2

We present an algorithm for finding temporally consistent occlusion boundaries in videos to support segmentation of dynamic scenes. We learn occlusion boundaries in a pairwise Mark…

cs.DS2013★ 7 cited

Tree-Independent Dual-Tree Algorithms

Ryan R. Curtin, William B. March, Parikshit Ram +3

Dual-tree algorithms are a widely used class of branch-and-bound algorithms. Unfortunately, developing dual-tree algorithms for use with different trees and problems is often compl…

cs.LG2008

Learning Isometric Separation Maps

Nikolaos Vasiloglou, Alexander G. Gray, David V. Anderson

Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensional spaces, often revealing the true intrinsic dimension. I…

cs.AI2008★ 4 cited

Non-Negative Matrix Factorization, Convexity and Isometry

Nikolaos Vasiloglou, Alexander G. Gray, David V. Anderson

In this paper we explore avenues for improving the reliability of dimensionality reduction methods such as Non-Negative Matrix Factorization (NMF) as interpretive exploratory data…