Heng Huang

Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting

Unsupervised domain adaptation (UDA) for nuclei instance segmentation is...
05/05/2020 ∙ by Dongnan Liu, et al. ∙ 19 ∙ share

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LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking

In this paper, we propose a novel effective light-weight framework, call...
05/07/2019 ∙ by Guanghan Ning, et al. ∙ 8 ∙ share

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Cell R-CNN V3: A Novel Panoptic Paradigm for Instance Segmentation in Biomedical Images

Instance segmentation is an important task for biomedical image analysis...
02/15/2020 ∙ by Dongnan Liu, et al. ∙ 6 ∙ share

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Scalable Semi-Supervised SVM via Triply Stochastic Gradients

Semi-supervised learning (SSL) plays an increasingly important role in t...
07/26/2019 ∙ by Xiang Geng, et al. ∙ 2 ∙ share

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Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a t...
09/05/2015 ∙ by Wenhao Jiang, et al. ∙ 0 ∙ share

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Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes

Image analysis using more than one modality (i.e. multi-modal) has been ...
10/31/2017 ∙ by Zhe Guo, et al. ∙ 0 ∙ share

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A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification

Linear Discriminant Analysis (LDA) is a widely-used supervised dimension...
10/14/2016 ∙ by Shuai Zheng, et al. ∙ 0 ∙ share

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An Iterative Locally Linear Embedding Algorithm

Local Linear embedding (LLE) is a popular dimension reduction method. In...
06/27/2012 ∙ by Deguang Kong, et al. ∙ 0 ∙ share

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Are Tensor Decomposition Solutions Unique? On the global convergence of HOSVD and ParaFac algorithms

For tensor decompositions such as HOSVD and ParaFac, the objective funct...
02/26/2009 ∙ by Dijun Luo, et al. ∙ 0 ∙ share

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Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding

Neural network based approaches for sentence relation modeling automatic...
03/30/2016 ∙ by Peng Li, et al. ∙ 0 ∙ share

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Clinical Information Extraction via Convolutional Neural Network

We report an implementation of a clinical information extraction tool th...
03/30/2016 ∙ by Peng Li, et al. ∙ 0 ∙ share

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Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics

In recent years, analyzing task-based fMRI (tfMRI) data has become an es...
10/24/2017 ∙ by Milad Makkie, et al. ∙ 0 ∙ share

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Decoupled Parallel Backpropagation with Convergence Guarantee

Backpropagation algorithm is indispensable for the training of feedforwa...
04/27/2018 ∙ by Zhouyuan Huo, et al. ∙ 0 ∙ share

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Training Neural Networks Using Features Replay

Training a neural network using backpropagation algorithm requires passi...
07/12/2018 ∙ by Zhouyuan Huo, et al. ∙ 0 ∙ share

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3D Global Convolutional Adversarial Network for Prostate MR Volume Segmentation

Advanced deep learning methods have been developed to conduct prostate M...
07/18/2018 ∙ by Haozhe Jia, et al. ∙ 0 ∙ share

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Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization

Proximal gradient method has been playing an important role to solve man...
02/16/2019 ∙ by Feihu Huang, et al. ∙ 0 ∙ share

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Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering

In this paper, we propose a novel end-to-end trainable Video Question An...
04/08/2019 ∙ by Chenyou Fan, et al. ∙ 0 ∙ share

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Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization

Alternating direction method of multipliers (ADMM) is a popular optimiza...
05/29/2019 ∙ by Feihu Huang, et al. ∙ 0 ∙ share

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Robust Linear Discriminant Analysis Using Ratio Minimization of L1,2-Norms

As one of the most popular linear subspace learning methods, the Linear ...
06/29/2019 ∙ by Feiping Nie, et al. ∙ 0 ∙ share

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An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms

This work aims at solving the problems with intractable sparsity-inducin...
07/02/2019 ∙ by Feiping Nie, et al. ∙ 0 ∙ share

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Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity

Zeroth-order (gradient-free) method is a class of powerful optimization ...
07/30/2019 ∙ by Feihu Huang, et al. ∙ 0 ∙ share

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Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization

Semi-supervised learning is pervasive in real-world applications, where ...
07/29/2019 ∙ by Wanli Shi, et al. ∙ 0 ∙ share

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Approaching Machine Learning Fairness through Adversarial Network

Fairness is becoming a rising concern w.r.t. machine learning model perf...
09/06/2019 ∙ by Xiaoqian Wang, et al. ∙ 0 ∙ share

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Diversely Stale Parameters for Efficient Training of CNNs

The backpropagation algorithm is the most popular algorithm training neu...
09/05/2019 ∙ by An Xu, et al. ∙ 0 ∙ share

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Ouroboros: On Accelerating Training of Transformer-Based Language Models

Language models are essential for natural language processing (NLP) task...
09/14/2019 ∙ by Qian Yang, et al. ∙ 0 ∙ share

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Straggler-Agnostic and Communication-Efficient Distributed Primal-Dual Algorithm for High-Dimensional Data Mining

Recently, reducing communication time between machines becomes the main ...
10/09/2019 ∙ by Zhouyuan Huo, et al. ∙ 0 ∙ share

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Region and Object based Panoptic Image Synthesis through Conditional GANs

Image-to-image translation is significant to many computer vision and ma...
12/14/2019 ∙ by Heng Wang, et al. ∙ 0 ∙ share

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Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization

Semi-supervised ordinal regression (S^2OR) problems are ubiquitous in re...
12/24/2019 ∙ by Wanli Shi, et al. ∙ 0 ∙ share

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Large Batch Training Does Not Need Warmup

Training deep neural networks using a large batch size has shown promisi...
02/04/2020 ∙ by Zhouyuan Huo, et al. ∙ 0 ∙ share

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Optimal Gradient Quantization Condition for Communication-Efficient Distributed Training

The communication of gradients is costly for training deep neural networ...
02/25/2020 ∙ by An Xu, et al. ∙ 0 ∙ share

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Exploit Where Optimizer Explores via Residuals

To train neural networks faster, many research efforts have been devoted...
04/11/2020 ∙ by An Xu, et al. ∙ 0 ∙ share

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Faster Secure Data Mining via Distributed Homomorphic Encryption

Due to the rising privacy demand in data mining, Homomorphic Encryption ...
06/17/2020 ∙ by Junyi Li, et al. ∙ 0 ∙ share

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Fast OSCAR and OWL Regression via Safe Screening Rules

Ordered Weighted L_1 (OWL) regularized regression is a new regression an...
06/29/2020 ∙ by Runxue Bao, et al. ∙ 0 ∙ share

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Heng Huang

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