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12th Meeting on Mathematics of Language, MOL 12

作者： Zhao, Qi Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab. School of Automation Science and Electrical Engineering Beihang University Beijing China Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9783642232107

Chinese language has been generally regarded as a Subject-Verb -Object (SVO) language and the basic semantic unit is the Chinese word that is usually consisted by two or more Chinese characters. However, word-centered structure of Chinese language has been controversial in linguistics. Some recent research in computational linguistics in Chinese language suggests that the character-based models perform better than the word-based models in some applications such word segmentation. In this paper, the word-based topic models and the character-based models are tested for modeling Chinese language, respectively. By empirical studies, we demonstrated the effectiveness of using Chinese characters as the basic semantic units. These two models have close performance in text classifications while the character-based model has a better quality in language modeling and a much smaller vocabulary. By testing on a bilingual corpus, three independent topic models based on Chinese words, Chinese characters and English words are trained and compared to each other. we verify the capability of topic models in modeling semantics by experiments across Chinese and English. The classification accuracy can also be boosted up by aggregating the classification results from the three independent topic models. © 2011 Springer-Verlag.

关键词： Computational linguistics

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Uncertainty Modeling for Data Mining 1

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丛书名： Advanced Topics in Science and Technology in China

1000年

作者： Zengchang Qin Yongchuan Tang

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Follow me up sports: New benchmark for 2d human keypoint recognition 2nd

Follow me up sports: New benchmark for 2d human keypoint rec...

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2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Huang, Ying Sun, Bin Kan, Haipeng Zhuang, Jiankai Qin, Zengchang Alibaba Business School Hangzhou Normal University Hangzhou China Keep Inc Beijing China Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China

ISBN: (纸本)9783030317256

Human pose estimation has made significant advancement in recent years. However, the existing datasets are limited in their coverage of pose variety. In this paper, we introduce a novel benchmark "FollowMeUp Sports" that makes an important advance in terms of specific postures, self-occlusion and class balance, a contribution that we feel is required for future development in human body models. This comprehensive dataset was collected using an established taxonomy of over 200 standard workout activities with three different shot angles. The collected videos cover a wider variety of specific workout activities than previous datasets including push-up, squat and body moving near the ground with severe self-occlusion or occluded by some sport equipment and outfits. Given these rich images, we perform a detailed analysis of the leading human pose estimation approaches gaining insights for the success and failures of these methods. © Springer Nature Switzerland AG 2019.

关键词： Sports

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Generative Cooperative Net for Image Generation and Data Augmentation 2018

arXiv

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arXiv 2017年

作者： Xu, Qiangeng Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab Beihang University Beijing China

How to build a good model for image generation given an abstract concept is a fundamental problem in computer vision. In this paper, we explore a generative model for the task of generating unseen images with desired features. We propose the Generative Cooperative Net (GCN) for image generation. The idea is similar to generative adversarial networks except that the generators and discriminators are trained to work accordingly. Our experiments on hand-written digit generation and facial expression generation show that GCN’s two cooperative counterparts (the generator and the classifier) can work together nicely and achieve promising results. We also discovered a usage of such generative model as an data-augmentation tool. Our experiment of applying this method on a recognition task shows that it is very effective comparing to other existing methods. It is easy to set up and could help generate a very large synthesized dataset. Copyright © 2017, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Deep learning for Prediction of Population of Acetes in Avoiding Biological Hazards for Nuclear Power Plants 14

Deep Learning for Prediction of Population of Acetes in Avoi...

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14th International Conference on intelligent Human-machine Systems and Cybernetics, IHMSC 2022

作者： Dai, Li Zhang, Rongyong Huang, Suyuan Liu, Junyi Li, Qi Zhang, Zhen Jiang, Xinshu Qin, Zengchang China Nuclear Power Engineering Co. Ltd Beijing China Beihang University Intelligent Computing and Machine Learning Lab School of Automation Science and Electrical Engineering Beijing China

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

There have been frequent incidents of water intake blockage due to marine organisms, which pose a serious threat to the normal operation of nuclear power plants across the world. In order to avoid biological hazards for Nuclear Power Plants, we investigated the disaster-caused marine organism. In this work, we focus on the acetes, which is the main cause of the accident. By investigating the biological characteristics of acetes, we have established a mathematical model of the population dynamics of acetes. We have also utilized two deep learning methods, LSTM and Transformer, to predict the population density of acetes. Finally, we have also compared the two methods. As a result, we find that LSTM performs better and it can be used for data-based dynamical modeling in future work. © 2022 IEEE.

关键词： Population statistics

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A k-hyperplane-based neural network for non-linear regression

A k-hyperplane-based neural network for non-linear regressio...

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IEEE International Conference on Cognitive Informatics

作者： He, Hongmei Qin, Zengchang Department of Engineering Mathematics University of Bristol Bristol United Kingdom Intelligent Computing and Machine Learning Lab. School of Automation and Electrical Engineering Beihang University Beijing 100191 China

ISBN: (纸本)9781424480401

For the time series prediction problem, the relationship between the abstracted independent variables and the response variable is usually strong non-linear. We propose a neural network fusion model based on k-hyperplanes for non-linear regression. A k-hyperplane clustering algorithm is developed to split the data to several clusters. The experiments are done on an artificial time series, and the convergence of k-hyperplane clustering algorithm and neural network gradient training algorithm is examined. The dimension of inputs affect the clustering performance very much. Neural network fusion can get some compensation in performance. It is shown that the prediction performance of the model for the time series is very good. The model can be further exploited for many real applications. © 2010 IEEE.

关键词： Clustering algorithms

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WAVELET-BASED STATISTICAL FEATURES FOR DISTINGUISHING MITOTIC AND NON-MITOTIC CELLS IN BREAST CANCER HISTOPATHOLOGY

WAVELET-BASED STATISTICAL FEATURES FOR DISTINGUISHING MITOTI...

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IEEE International Conference on Image Processing

作者： Tao Wan Xu Liu Jianhui Chen Zengchang Qin Intelligent Computing and Machine Learning Lab Beihang University No 91 Central Hospital of PLA

ISBN: (纸本)9781479957521

To diagnose breast cancer (BCa), the number of mitotic cells present in tissue sections is an important parameter to examine and grade breast biopsy specimen. The differentiation of mitotic from non-mitotic cells in breast histopatho-logical images is a crucial step for automatical mitosis detection. This work aims at improving the accuracy of mitosis classification by characterizing objects of interest (tissue cells) in wavelet based multi-resolution representations that better capture the statistical features having mitosis discrimination. A dual-tree complex wavelet transform (DT-CWT) is performed to decompose the image patches into multi-scale forms. Five commonly-used statistical features are extracted on each wavelet subband. Since both mitotic and non-mitotic cells appear as small objects with a large variety of shapes in the images, characterization of mitosis is a challenging problem. The inter-scale dependencies of wavelet coefficients allow extraction of important texture features within the cells that are more likely to appear at all different scales. The wavelet-based statistical features were evaluated on a dataset containing 327 mitotic and 406 non-mitotic cells via a support vector machine classifier in iterative cross-validation. The quantitative results showed that our DT-CWT based approach achieved superior classification performance with the accuracy of 87.94%, sensitivity of 86.80%, specificity of 89.89%, and the area under the curve (AUC) value of 0.94.

关键词： Mitosis Wavelet transform Multi-resolution representation Breast cancer histopathology

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Wavelet-based statistical features for distinguishing mitotic and non-mitotic cells in breast cancer histopathology

Wavelet-based statistical features for distinguishing mitoti...

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作者： Wan, Tao Liu, Xu Chen, Jianhui Qin, Zengchang Intelligent Computing and Machine Learning Lab Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China No 91 Central Hospital of PLA Henan454003 China

ISBN: (纸本)9781479957514

To diagnose breast cancer (BCa), the number of mitotic cells present in tissue sections is an important parameter to examine and grade breast biopsy specimen. The differentiation of mitotic from non-mitotic cells in breast histopathological images is a crucial step for automatical mitosis detection. This work aims at improving the accuracy of mitosis classification by characterizing objects of interest (tissue cells) in wavelet based multi-resolution representations that better capture the statistical features having mitosis discrimination. A dual-tree complex wavelet transform (DT-CWT) is performed to decompose the image patches into multi-scale forms. Five commonly-used statistical features are extracted on each wavelet subband. Since both mitotic and non-mitotic cells appear as small objects with a large variety of shapes in the images, characterization of mitosis is a challenging problem. The inter-scale dependencies of wavelet coefficients allow extraction of important texture features within the cells that are more likely to appear at all different scales. The wavelet-based statistical features were evaluated on a dataset containing 327 mitotic and 406 non-mitotic cells via a support vector machine classifier in iterative cross-validation. The quantitative results showed that our DT-CWT based approach achieved superior classification performance with the accuracy of 87.94%, sensitivity of 86.80%, specificity of 89.89%, and the area under the curve (AUC) value of 0.94. © 2014 IEEE.

关键词： Wavelet transforms

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Can Gaussian Sketching Converge Faster on a Preconditioned Landscape? 41

Can Gaussian Sketching Converge Faster on a Preconditioned L...

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41st International Conference on machine learning, ICML 2024

作者： Wang, Yilong Ye, Haishan Dai, Guang Tsang, Ivor W. Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China SGIT AI Lab State Grid Corporation of China China Singapore College of Computing and Data Science NTU Singapore

This paper focuses on the large-scale optimization which is very popular in the big data era. The gradient sketching is an important technique in the large-scale optimization. Specifically, the random coordinate descent algorithm is a kind of gradient sketching method with the random sampling matrix as the sketching matrix. In this paper, we propose a novel gradient sketching called GSGD (Gaussian Sketched Gradient Descent). Compared with the classical gradient sketching methods such as the random coordinate descent and SEGA (Hanzely et al., 2018), our GSGD does not require the importance sampling but can achieve a fast convergence rate matching the ones of these methods with importance sampling. Furthermore, if the objective function has a non-smooth regularization term, our GSGD can also exploit the implicit structure information of the regularization term to achieve a fast convergence rate. Finally, our experimental results substantiate the effectiveness and efficiency of our algorithm. Copyright 2024 by the author(s)

关键词： Big data

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Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods 41

Double Stochasticity Gazes Faster: Snap-Shot Decentralized S...

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41st International Conference on machine learning, ICML 2024

作者： Di, Hao Ye, Haishan Chang, Xiangyu Dai, Guang Tsang, Ivor W. Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China SGIT AI Lab State Grid Corporation of China China College of Computing and Data Science NTU Singapore Singapore

In decentralized optimization, m agents form a network and only communicate with their neighbors, which gives advantages in data ownership, privacy, and scalability. At the same time, decentralized stochastic gradient descent (SGD) methods, as popular decentralized algorithms for training large-scale machine learning models, have shown their superiority over centralized counterparts. Distributed stochastic gradient tracking (DSGT) (Pu & Nedić, 2021) has been recognized as the popular and state-of-the-art decentralized SGD method due to its proper theoretical guarantees. However, the theoretical analysis of DSGT (Koloskova et al., 2021) shows that its iteration complexity is (equation presented) where the doubly stochastic matrix W represents the network topology and CW is a parameter that depends on W. Thus, it indicates that the convergence property of DSGT is heavily affected by the topology of the communication network. To overcome the weakness of DSGT, we resort to the snapshot gradient tracking skill and propose two novel algorithms, snap-shot DSGT (SS DSGT) and accelerated snap-shot DSGT (ASS DSGT). We further justify that SS DSGT exhibits a lower iteration complexity compared to DSGT in the general communication network topology. Additionally, ASS DSGT matches DSGT's iteration complexity (equation presented) under the same conditions as DSGT. Numerical experiments validate SS DSGT's superior performance in the general communication network topology and exhibit better practical performance of ASS DSGT on the specified W compared to DSGT. Copyright 2024 by the author(s)

关键词： Network topology

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