no code implementations • 13 Jul 2023 • Yuanhang Zhang, Jundong Liu
Path planning plays a crucial role in various autonomy applications, and RRT* is one of the leading solutions in this field.
no code implementations • 10 Mar 2023 • Marc Baltes, Nidal Abujahar, Ye Yue, Charles D. Smith, Jundong Liu
The field of machine learning has been greatly transformed with the advancement of deep artificial neural networks (ANNs) and the increased availability of annotated data.
no code implementations • 14 Feb 2023 • Ye Yue, Marc Baltes, Nidal Abujahar, Tao Sun, Charles D. Smith, Trevor Bihl, Jundong Liu
Over the past decade, artificial neural networks (ANNs) have made tremendous advances, in part due to the increased availability of annotated data.
no code implementations • 12 Oct 2022 • Tao Sun, Nidal Abuhajar, Shuyu Gong, Zhewei Wang, Charles D. Smith, Xianhui Wang, Li Xu, Jundong Liu
Speaker separation aims to extract multiple voices from a mixed signal.
no code implementations • 12 Oct 2022 • Sirui Song, Kirk Saunders, Ye Yue, Jundong Liu
In this work, we proposed several novel agent state and reward function designs to tackle two critical issues in DRL-based navigation solutions: 1) smoothness of the trained flight trajectories; and 2) model generalization to handle unseen environments.
no code implementations • 12 Oct 2022 • Binhua Liao, Yani Chen, Zhewei Wang, Charles D. Smith, Jundong Liu
In this paper, we explore the capabilities of a number of deep neural network models in generating whole-brain 3T-like MR images from clinical 1. 5T MRIs.
1 code implementation • 19 Sep 2021 • Li Pan, Jundong Liu, Mingqin Shi, Chi Wah Wong, Kei Hang Katie Chan
To further recalibrate the distribution of the extracted features under phenotypic information, we subsequently embed the sparse feature vectors into a population graph, where the hidden inter-subject heterogeneity and homogeneity are explicitly expressed as inter- and intra-community connectivity differences, and utilize Graph Convolutional Networks to learn the node embeddings.
no code implementations • ICLR 2019 • Tao Sun, Zhewei Wang, C. D. Smith, Jundong Liu
In this paper, we propose a capsule-based neural network model to solve the semantic segmentation problem.
no code implementations • 11 Jan 2019 • Zhewei Wang, Weizhen Cai, Charles D. Smith, Noriko Kantake, Thomas J. Rosol, Jundong Liu
In this paper, we propose a pyramid network structure to improve the FCN-based segmentation solutions and apply it to label thyroid follicles in histology images.
no code implementations • ICLR 2019 • Tao Sun, Zhewei Wang, C. D. Smith, Jundong Liu
We model this procedure as a traceback pipeline and take it as a central piece to build an end-to-end segmentation network.
no code implementations • 24 Jul 2018 • Zhewei Wang, Charles D. Smith, Jundong Liu
In this paper, we develop a two-stage neural network solution for the challenging task of white-matter lesion segmentation.
no code implementations • 12 May 2018 • Zhewei Wang, Bibo Shi, Charles D. Smith, Jundong Liu
In this paper, we propose a nonlinear distance metric learning scheme based on the fusion of component linear metrics.
no code implementations • 6 Aug 2015 • Bibo Shi, Jundong Liu
In recent years, research efforts to extend linear metric learning models to handle nonlinear structures have attracted great interests.