Search Results for author: Dong Guo

Found 8 papers, 0 papers with code

Annotation-Efficient Learning for Medical Image Segmentation based on Noisy Pseudo Labels and Adversarial Learning

no code implementations29 Dec 2020 Lu Wang, Dong Guo, Guotai Wang, Shaoting Zhang

In this paper, we propose an annotation-efficient learning framework for segmentation tasks that avoids annotations of training images, where we use an improved Cycle-Consistent Generative Adversarial Network (GAN) to learn from a set of unpaired medical images and auxiliary masks obtained either from a shape model or public datasets.

Medical Image Segmentation

Large-Scale Unsupervised Deep Representation Learning for Brain Structure

no code implementations2 May 2018 Ayush Jaiswal, Dong Guo, Cauligi S. Raghavendra, Paul Thompson

Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data.

Representation Learning

Unifying Local and Global Change Detection in Dynamic Networks

no code implementations9 Oct 2017 Wenzhe Li, Dong Guo, Greg Ver Steeg, Aram Galstyan

Many real-world networks are complex dynamical systems, where both local (e. g., changing node attributes) and global (e. g., changing network topology) processes unfold over time.

Kernel Approximation Methods for Speech Recognition

no code implementations13 Jan 2017 Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha

First, in order to reduce the number of random features required by kernel models, we propose a simple but effective method for feature selection.

Feature Selection Speech Recognition

Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization

no code implementations13 Nov 2014 Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan

We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots.

Global Optimization Link Prediction

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