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6th International Conference on Machine Learning Technologies (ICMLT)

作者： Vardhan, Harsh Sztipanovits, Janos Vanderbilt Univ Tute Software Integrated Syste 221 Kirkland Hall Nashville TN 37235 USA

ISBN: (纸本)9781450389402

Machine learning models have prevalent applications in many real-world problems, which increases the importance of correctness in the behaviour of these trained models. Finding a good test case that can reveal the potential failure in these trained systems can help to retrain these models to increase the correctness. For a well-trained model, the occurrence of a failure is rare. Consequently, searching these rare scenarios by evaluating each sample in input search space or randomized search would be costly and sometimes intractable due to large search space, limited computational resources, and available time. In this paper, we tried to address this challenge of finding these failure scenarios faster than traditional randomized search. The central idea of our approach is to separate the input data space in region of high failure probability and region of low/minimal failure probability based on the observation made by training data, data drawn from real-world statistics, and knowledge from a domain expert. Using these pieces of information, we can design a generative model from which we can generate scenarios that have a high likelihood to reveal the potential failure. We evaluated this approach on two different experimental scenarios and able to speed up the discovery of such failures a thousand-fold faster than the traditional randomized search.

关键词： Machine learning failure test scenarios reinforcement learning probabilistic generative model Bayesian machine learning Latent variable model rare event failure

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Multi-modal Bayesian embedding for point-of-interest recommendation on location-based cyber-physical-social networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 108卷 1119-1128页

作者： Huang, Liwei Ma, Yutao Liu, Yanbo Sangaiah, Arun Kumar Beijing Inst Remote Sensing Beijing 100854 Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

Point-of-interest (POI) recommendation has become a favorite topic on location-based cyber-physicalsocial networks (LBCPSNs). The geographical influence of locations (e.g., geographical proximity) and the social influence of friends (e.g., social ties) have been widely used in many existing POI recommendation methods, most of which paid little attention to the sequential influence and temporal influence of locations on users' check-in behaviors. However, human mobility exhibits sequential and temporal patterns. In this paper, we propose a unified probabilistic generative model, i.e., a multi-modal Bayesian embedding model (MMBE), to discover the social, sequential, temporal, and spatial patterns of users' check-in behaviors simultaneously. Besides, MMBE can model the joint effect of the four factors mentioned above on the decision-making for POI selection across heterogeneous workflow activities in cyber-physicalsocial (CPS) systems. Then, we conduct an in-depth performance evaluation for MMBE using two largescale real-world datasets from Gowalla and Brightkite. Experimental results show that our proposed method outperforms the other five state-of-the-art POI recommendation approaches regarding Precision and Recall. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Point-of-interest recommendation Location-based social networks Multi-modal information probabilistic generative model Cyber-physical-social systems

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CVTM: A Content-Venue-Aware Topic model for Group Event Recommendation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第7期32卷 1290-1303页

作者： Du, Yulu Meng, Xiangwu Zhang, Yujie Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

Event recommendation is essential to help people find attractive events to attend, but it intrinsically faces cold-start problem. The previous studies exploit multiple contextual factors to overcome the cold-start problem in event recommendation. However, they do not consider the correlation among different contextual factors. Moreover, suggesting events for a group of users also has not been well studied. In this paper, we first discover the correlation between organizer and textual content, i.e., the events held by the same organizer tend to have more similar content. Based on this observation, we present a content-venue-aware topic model (CVTM) to capture group interests on an event from two perspectives: content and venue. The correlation between organizer and content is modeled in CVTM to alleviate the sparsity of textual content, and then we can further extract group interests on content of an event more accurately. Finally, a group event recommendation method using CVTM is proposed. We conduct comprehensive experiments to evaluate the recommendation performance of our model on two real-world datasets. The results demonstrate that the proposed model outperforms the state-of-the-art methods that suggest upcoming events for groups. Besides, CVTM can learn semantically coherent latent topics which are useful to explain recommendations.

关键词： Correlation Games Context modeling Social networking (online) probabilistic logic Recommender systems Task analysis Group recommendation probabilistic generative model event recommendation event-based social networks

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probabilistic Weighted Copula Regression model With Adaptive Sample Selection Strategy for Complex Industrial Processes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第11期16卷 6972-6981页

作者： Zhou, Yang Ren, Xiang Li, Shaojun East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Rutgers State Univ Dept Chem & Biochem Engn Piscataway NJ 08854 USA

With complicated structures and complex physicochemical reactions, most industrial processes are intrinsically characterized by high dimensionality, nonlinearity, non-Gaussianity, and self-correlation. In this article, a novel probabilistic generative model, called weighted copula regression (WCR), is developed for complex processes. This method employs a probabilistic graphical tool (vine copula) to flexibly handle the underlying patterns via the factorization of complex dependence structures into multiple bivariate copulas. Monte Carlo estimation is incorporated for establishing a fast and reliable computing framework. To avoid overinterpretation of the industrial data, an adaptive sample selection strategy is proposed to explore the underlying distribution of the sample space and to select the most "informative" samples. By considering the copula weights that carry crucial information on the local data, the probabilistic WCR method can provide fast point estimate with prediction uncertainty for every predicted sample. The proposed WCR method is compared with six state-of-the-art methods, and the efficiency is validated using a numerical example and the ethylene cracking furnace process.

关键词： probabilistic logic Estimation Training Adaptation models Data models Probability density function Distribution functions Active learning industrial process Monte Carlo estimation probabilistic generative model probabilistic graphical tool soft sensor vine copula

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FusionNet: An Unsupervised Convolutional Variational Network for Hyperspectral and Multispectral Image Fusion

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 7565-7577页

作者： Wang, Zhengjue Chen, Bo Lu, Ruiying Zhang, Hao Liu, Hongwei Varshney, Pramod K. Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Natl Lab Radar Signal Proc Xian 710071 Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

Due to hardware limitations of the imaging sensors, it is challenging to acquire images of high resolution in both spatial and spectral domains. Fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to obtain an HR-HSI in an unsupervised manner has drawn considerable attention. Though effective, most existing fusion methods are limited due to the use of linear parametric modeling for the spectral mixture process, and even the deep learning-based methods only focus on deterministic fully-connected networks without exploiting the spatial correlation and local spectral structures of the images. In this paper, we propose a novel variational probabilistic autoencoder framework implemented by convolutional neural networks, in order to fuse the spatial and spectral information contained in the LR-HSI and HR-MSI, called FusionNet. The FusionNet consists of a spectral generative network, a spatial-dependent prior network, and a spatial-spectral variational inference network, which are jointly optimized in an unsupervised manner, leading to an end-to-end fusion system. Further, for fast adaptation to different observation scenes, we give a meta-learning explanation to the fusion problem, and combine the FusionNet with meta-learning in a synergistic manner. Effectiveness and efficiency of the proposed method are evaluated based on several publicly available datasets, demonstrating that the proposed FusionNet outperforms the state-of-the-art fusion methods.

关键词： Hyperspectral images multispectral images image fusion probabilistic generative model convolutional neural network meta-learning

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Learning probabilistic generative models for Fast Sampling-based Planning

Learning Probabilistic Generative Models for Fast Sampling-b...

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作者： Huh, Jinwook University of Pennsylvania

学位级别：Ph.D.

Due to their simplicity and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present several new learning approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision detection in high dimensional configuration spaces based on Gaussian Mixture models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new probabilistically safe local steering primitive based on the probabilistic model. Our local steering procedure is based on a new notion of a convex probabilistically safety corridor that is constructed around a configuration using tangent hyperplanes of confidence ellipsoids of GMMs learned from prior collision history. For efficient sampling, we suggest a sampling method with a learned Q-function with linear function approximation based on feature representations such as Radial Basis Functions. This sampling method chooses the optimal node from which to extend the search tree via the softmax function of learned state values. We also discuss a novel constrained sampling-based motion planning method for grasp and transport tasks with redundant robotic manipulators, which allows the best grasp configuration and approach direction to be automatically determined. Since these approaches with the learned probabilistic models require large size data and time for training, it is essential that they are able to be adapted to environmental change in an online manner. The suggested online learning approach with the Dirichlet Process Mixture model (DPMM) can adapt the complexity to the data and learn new Gaussian clusters with streaming data in newly explored areas without batch learning. We have applied these approaches in a number of robot arm planning scenarios and have shown their utility and effectiveness in simulation and on a physical 7-DoF robot manipulator.

关键词： Gaussian mixture model Machine learning Motion and path planning probabilistic generative model Robotics Sampling-based planning

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Using an optimized generative model to infer the progression of complications in type 2 diabetes patients

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2022年第1期22卷 174-174页

作者： Wang, Xiaoxia Lin, Yifei Xiong, Yun Zhang, Suhua He, Yanming He, Yuqing Zhang, Zhikun Plasek, Joseph M. Zhou, Li Bates, David W. Tang, Chunlei Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai 200438 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Gansu Peoples R China Sichuan Univ West China Hosp Chengdu 610041 Sichuan Peoples R China Shanghai Jiao Tong Univ Suzhou Kowloon Hosp Dept Kidney Dis Sch Med Shanghai 215021 Jiangsu Peoples R China Shanghai Univ Tradit Chinese Med Yueyang Hosp Integrated Tradit Chinese Med & West Dept Endocrinol Shanghai 200437 Peoples R China Fudan Univ Sch Econ Inst Data Ind Shanghai 200433 Peoples R China Harvard Med Sch Brigham & Womens Hosp Div Gen Internal Med & Primary Care Boston MA 02115 USA Mass Gen Brigham Clin & Qual Anal Boston MA 02145 USA Brigham & Womens Hosp Div Gen Internal Med & Primary Care 1620 Tremont St Boston MA 02120 USA

Background: People live a long time in pre-diabetes/early diabetes without a formal diagnosis or management. Heterogeneity of progression coupled with deficiencies in electronic health records related to incomplete data, discrete events, and irregular event intervals make identification of pre-diabetes and critical points of diabetes progression challenging. Methods: We utilized longitudinal electronic health records of 9298 patients with type 2 diabetes or prediabetes from 2005 to 2016 from a large regional healthcare delivery network in China. We optimized a generative Markov-Bayesian-based model to generate 5000 synthetic illness trajectories. The synthetic data were manually reviewed by endocrinologists. Results: We build an optimized generative progression model for type 2 diabetes using anchor information to reduce the number of parameters learning in the third layer of the model from O(N x W) to O((N - C) x W), where N is the number of clinical findings, W is the number of complications, C is the number of anchors. Based on this model, we infer the relationships between progression stages, the onset of complication categories, and the associated diagnoses during the whole progression of type 2 diabetes using electronic health records. Discussion: Our findings indicate that 55.3% of single complications and 31.8% of complication patterns could be predicted early and managed appropriately to potentially delay (as it is a progressive disease) or prevented (by lifestyle modifications that keep patient from developing/triggering diabetes in the first place). Conclusions: The full type 2 diabetes patient trajectories generated by the chronic disease progression model can counter a lack of real-world evidence of desired longitudinal timeframe while facilitating population health management.

关键词： Computer simulation Disease progression model Diabetes mellitus type 2 probabilistic generative model Electronic health records

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Variational probabilistic generative framework for single image super-resolution

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SIGNAL PROCESSING 2019年 156卷 92-105页

作者： Wang, Zhengjue Chen, Bo Zhang, Hao Liu, Hongwei Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Xian 710071 Shaanxi Peoples R China

In this paper, a general variational probabilistic generative framework parameterized by deep networks is proposed for single image super-resolution, which assembles the advantages of coding-based methods and regression-based methods. We use probabilistic generative networks to model the joint full likelihood of a pair of low-resolution (LR) and high-resolution (HR) patches which are generated from a shared latent representation. An inference model is applied to infer the stochastic distribution of the latent representation. By jointly optimizing the generative and inference models, a regression process to the distribution of the HR patch is implied during the learning phase, which provides an efficient forward mapping to accomplish the super-resolution task. We use our framework as a guidance and develop a new model called PGM-CP, with the help of an informative conditional prior and a consistent recognition model. We likewise show how three existing popular example-based SR methods can be "reinvented" under our framework. The effectiveness and efficiency of the proposed method is examined based on three public datasets. Experimental results demonstrate that our model is competitive with state-of-the-art approaches, especially when the image is corrupted by noise. (C) 2018 Elsevier B.V. All rights reserved.

关键词： probabilistic generative model Image super-resolution Conditional prior Recognition model

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BoRe: Adapting to Reader Consumption Behavior Instability for News Recommendation

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2020年第1期38卷 3-3页

作者： Lv, Pengtao Meng, Xiangwu Zhang, Yujie Beijing Univ Posts & Telecommun Sch Comp Sci 10 Xi Tucheng Rd Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & 10 Xi Tucheng Rd Beijing 100876 Peoples R China

News recommendation has become an essential way to help readers discover interesting stories. While a growing line of research has focused on modeling reading preferences for news recommendation, they neglect the instability of reader consumption behaviors, i.e., consumption behaviors of readers may be influenced by other factors in addition to user interests, which degrades the recommendation effectiveness of existing methods. In this article, we propose a probabilistic generative model, BoRe, where user interests and crowd effects are used to adapt to the instability of reader consumption behaviors, and reading sequences are utilized to adapt user interests evolving over time. Further, the extreme sparsity problem in the domain of news severely hinders accurately modeling user interests and reading sequences, which discounts BoRe's ability to adapt to the instability. Accordingly, we leverage domain-specific features to model user interests in the situation of extreme sparsity. Meanwhile, we consider groups of users instead of individuals to capture reading sequences. Besides, we study how to reduce the computation to allow online application. Extensive experiments have been conducted to evaluate the effectiveness and efficiency of BoRe on real-world datasets. The experimental results show the superiority of BoRe, compared with the state-of-the-art competing methods.

关键词： News recommendation reader consumption behavior instability domain-specific feature probabilistic generative model

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RAFnet: Recurrent attention fusion network of hyperspectral and multispectral images

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SIGNAL PROCESSING 2020年 177卷 107737-107737页

作者： Lu, Ruiying Chen, Bo Cheng, Ziheng Wang, Penghui Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Hyperspectral imaging can facilitate a better understanding of more knowledge under real scenes, compared with traditional image systems. However, due to hardware limitations, only low resolution hyperspectral (LrHs) and high resolution multispectral (HrMs) images can generally be acquired. This paper proposed a recurrent attention fusion network (RAFnet) under a variational probabilistic generative framework, in order to fuse the LrHs and HrMs images together to generate a high resolution hyperspectral (HrHs) image in an unsupervised manner. In specific, two variational autoencoders are designed to preserve both spectral and spatial information of LrHs and HrMs images, coupled through a shared decoder to generate hyperspectral images. Considering the spectra of each hyperspectral pixel is intrinsically a sequence based data structure, we construct a hierarchical recurrent neural network to extract the abundant spectral information. Moreover, self-attention and relation-attention mechanisms are adopted to capture long temporal dependencies through the spectral domain. The effectiveness and efficiency are evaluated based on several publicly available hyperspectral datasets, compared with many state-of-the-art methods for the unsupervised fusion task. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Hyperspectral images Multispectral images Image fusion probabilistic generative model Recurrent neural network Attention

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