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Supervised Machine Learning with a Novel Kernel Density Estimator

Oct 16, 2007

Yen-Jen Oyang, Darby Tien-Hao Chang, Yu-Yen Ou, Hao-Geng Hung, Chih-Peng Wu, Chien-Yu Chen

In recent years, kernel density estimation has been exploited by computer scientists to model machine learning problems. The kernel density estimation based approaches are of interest due to the low time complexity of either O(n) or O(n*log(n)) for constructing a classifier, where n is the number of sampling instances. Concerning design of kernel density estimators, one essential issue is how fast the pointwise mean square error (MSE) and/or the integrated mean square error (IMSE) diminish as the number of sampling instances increases. In this article, it is shown that with the proposed kernel function it is feasible to make the pointwise MSE of the density estimator converge at O(n^-2/3) regardless of the dimension of the vector space, provided that the probability density function at the point of interest meets certain conditions.
Oct 16, 2007

Yen-Jen Oyang, Darby Tien-Hao Chang, Yu-Yen Ou, Hao-Geng Hung, Chih-Peng Wu, Chien-Yu Chen

* The new version includes an additional theorem, Theorem 3

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Boltzmann Generators - Sampling Equilibrium States of Many-Body Systems with Deep Learning

Dec 04, 2018

Frank Noé, Hao Wu

Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples directly, vast computational effort is invested for simulating these system in small steps, e.g., using Molecular Dynamics. Combining deep learning and statistical mechanics, we here develop Boltzmann Generators, that are shown to generate statistically independent samples of equilibrium states of representative condensed matter systems and complex polymers. Boltzmann Generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free energy differences, and discovery of new system states are demonstrated, providing a new statistical mechanics tool that performs orders of magnitude faster than standard simulation methods.
Dec 04, 2018

Frank Noé, Hao Wu

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Variational approach for learning Markov processes from time series data

Dec 11, 2017

Hao Wu, Frank Noé

Dec 11, 2017

Hao Wu, Frank Noé

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Network Vector: Distributed Representations of Networks with Global Context

Sep 07, 2017

Hao Wu, Kristina Lerman

Sep 07, 2017

Hao Wu, Kristina Lerman

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* Proceedings of the 29th conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016, pp. 4179-4187

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* 12 pages, 8 figures, 3 tables, Second International Conference on Computer Science and Information Technology (COSIT 2015) March 21~22, 2015, Geneva, Switzerland

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HPILN: A feature learning framework for cross-modality person re-identification

Jun 07, 2019

Jian-Wu Lin, Hao Li

Jun 07, 2019

Jian-Wu Lin, Hao Li

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* 66 pages, 15 figures

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AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference

May 24, 2018

Jian-Hao Luo, Jianxin Wu

May 24, 2018

Jian-Hao Luo, Jianxin Wu

* Submitted to NIPS 2018

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Learning Effective Binary Visual Representations with Deep Networks

Mar 08, 2018

Jianxin Wu, Jian-Hao Luo

Mar 08, 2018

Jianxin Wu, Jian-Hao Luo

* 16 pages, 3 figures

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**Click to Read Paper and Get Code**

Uneven illumination surface defects inspection based on convolutional neural network

May 16, 2019

Hao Wu, Xiangrong Xu, Wenbin Gao

Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination.
May 16, 2019

Hao Wu, Xiangrong Xu, Wenbin Gao

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A Multi-Axis Annotation Scheme for Event Temporal Relations

May 14, 2018

Qiang Ning, Hao Wu, Dan Roth

May 14, 2018

Qiang Ning, Hao Wu, Dan Roth

* [Final Version] 14 pages, accepted by ACL'18

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ResumeVis: A Visual Analytics System to Discover Semantic Information in Semi-structured Resume Data

May 15, 2017

Chen Zhang, Hao Wang, Yingcai Wu

May 15, 2017

Chen Zhang, Hao Wang, Yingcai Wu

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Sparse Estimation of Multivariate Poisson Log-Normal Models from Count Data

Aug 12, 2016

Hao Wu, Xinwei Deng, Naren Ramakrishnan

Aug 12, 2016

Hao Wu, Xinwei Deng, Naren Ramakrishnan

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Modeling Coherence for Discourse Neural Machine Translation

Nov 14, 2018

Hao Xiong, Zhongjun He, Hua Wu, Haifeng Wang

Nov 14, 2018

Hao Xiong, Zhongjun He, Hua Wu, Haifeng Wang

* AAAI2019

* Accepted by AAAI2019

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