Deep Multiple Description Coding by Learning Scalar Quantization

Nov 05, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

Nov 05, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

* 8 pages, 4 figures

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Virtual Codec Supervised Re-Sampling Network for Image Compression

Jul 10, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

Jul 10, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

* 13 pages, 11 figures Our project can be found in the website: https://github.com/VirtualCodecNetwork

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Multiple Description Convolutional Neural Networks for Image Compression

Jan 20, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

Jan 20, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

* 13 pages, 3 tables, and 6 figures

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Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image

Jan 16, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

Jan 16, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

* 11 pages, 7 figures

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Mixed-Resolution Image Representation and Compression with Convolutional Neural Networks

Aug 01, 2018

Lijun Zhao, Huihui Bai, Feng Li, Anhong Wang, Yao Zhao

Aug 01, 2018

Lijun Zhao, Huihui Bai, Feng Li, Anhong Wang, Yao Zhao

* 5 pages, and 2 figures. arXiv admin note: substantial text overlap with arXiv:1712.05969

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Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing

Nov 18, 2017

Lijun Zhao, Jie Liang, Huihui Bai, Lili Meng, Anhong Wang, Yao Zhao

Nov 18, 2017

Lijun Zhao, Jie Liang, Huihui Bai, Lili Meng, Anhong Wang, Yao Zhao

* 13 papers, 9 figures

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Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network

Nov 08, 2017

Lijun Zhao, Huihui Bai, Jie Liang, Bing Zeng, Anhong Wang, Yao Zhao

Nov 08, 2017

Lijun Zhao, Huihui Bai, Jie Liang, Bing Zeng, Anhong Wang, Yao Zhao

* 11 pages, 10 figures

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On the ERM Principle with Networked Data

Nov 22, 2017

Yuanhong Wang, Yuyi Wang, Xingwu Liu, Juhua Pu

Nov 22, 2017

Yuanhong Wang, Yuyi Wang, Xingwu Liu, Juhua Pu

* accepted by AAAI. arXiv admin note: substantial text overlap with arXiv:math/0702683 by other authors

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Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes

Feb 18, 2017

Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang

In this work we study the quantitative relation between the recursive teaching dimension (RTD) and the VC dimension (VCD) of concept classes of finite sizes. The RTD of a concept class $\mathcal C \subseteq \{0, 1\}^n$, introduced by Zilles et al. (2011), is a combinatorial complexity measure characterized by the worst-case number of examples necessary to identify a concept in $\mathcal C$ according to the recursive teaching model. For any finite concept class $\mathcal C \subseteq \{0,1\}^n$ with $\mathrm{VCD}(\mathcal C)=d$, Simon & Zilles (2015) posed an open problem $\mathrm{RTD}(\mathcal C) = O(d)$, i.e., is RTD linearly upper bounded by VCD? Previously, the best known result is an exponential upper bound $\mathrm{RTD}(\mathcal C) = O(d \cdot 2^d)$, due to Chen et al. (2016). In this paper, we show a quadratic upper bound: $\mathrm{RTD}(\mathcal C) = O(d^2)$, much closer to an answer to the open problem. We also discuss the challenges in fully solving the problem.
Feb 18, 2017

Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang

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Spatio-temporal Aware Non-negative Component Representation for Action Recognition

Aug 27, 2016

Jianhong Wang, Tian Lan, Xu Zhang, Limin Luo

Aug 27, 2016

Jianhong Wang, Tian Lan, Xu Zhang, Limin Luo

* 11 pages, 5 figures, 6 tables

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Quantum Speedup in Adaptive Boosting of Binary Classification

Feb 03, 2019

Ximing Wang, Yuechi Ma, Min-Hsiu Hsieh, Manhong Yung

Feb 03, 2019

Ximing Wang, Yuechi Ma, Min-Hsiu Hsieh, Manhong Yung

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Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning

May 28, 2018

Rui Luo, Yaodong Yang, Jianhong Wang, Zhanxing Zhu, Jun Wang

In this paper, we propose a novel sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for the purpose of multimodal Bayesian learning. It simulates a noisy dynamical system by incorporating both a continuously-varying tempering variable and the Nos\'e-Hoover thermostats. A significant benefit is that it is not only able to efficiently generate i.i.d. samples when the underlying posterior distributions are multimodal, but also capable of adaptively neutralising the noise arising from the use of mini-batches. While the properties of the approach have been studied using synthetic datasets, our experiments on three real datasets have also shown its performance gains over several strong baselines for Bayesian learning with various types of neural networks plunged in.
May 28, 2018

Rui Luo, Yaodong Yang, Jianhong Wang, Zhanxing Zhu, Jun Wang

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Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams

Jan 17, 2018

Lei Chu, Robert Qiu, Haichun Liu, Zenan Ling, Tianhong Zhang, Jijun Wang

Jan 17, 2018

Lei Chu, Robert Qiu, Haichun Liu, Zenan Ling, Tianhong Zhang, Jijun Wang

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