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7th ACM India Joint International Conference on Data Science and Management of Data (CODS-COMAD) / 11th ACM IKDD CODS Conference / 29th COMAD Conference

作者： Mukherjee, Debayan Bandyopadhyay, Atreya Datta, Soham Bhattacharya, Indrajit TCS Res Chennai Tamil Nadu India

ISBN: (纸本)9798400716348

Data in enterprises resides largely under relational schemas. Annotating such schemas with a knowledge graph (KG) that represents knowledge of the domain is useful for semantic understanding of the data as well as downstream processing by machines and humans. Existing approaches annotate only individual tables using small and simple KGs, and also fail to generalize to unseen KG entities and relationships during test. We propose a probabilistic model that generates complex relational schemas - tables, grouping of tables into neighborhoods, foreign key connections between tables and fields associated with tables - by traversing over paths in a knowledge graph. An efficient two-pass inference algorithm based on inverting this model jointly annotates schema elements such as fields, tables and neighborhoods with entities, and the associations between schema elements with relational paths in the KG. The algorithm also generalizes to unseen paths at test time. We show using experiments on a real-world schema and domain knowledge graph, in addition to benchmark datasets, that the proposed approach significantly out-performs existing approaches while demonstrating better scalability.

关键词： relational schema semantic annotation knowledge graph probabilistic generative model

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Unsupervised Hyperspectral and Multispectral Images Fusion Based on Nonlinear Variational probabilistic generative model

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第2期33卷 721-735页

作者： Wang, Zhengjue Chen, Bo Zhang, Hao Liu, Hongwei Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Due to hardware limitations, it is challenging for sensors to acquire images of high resolution in both spatial and spectral domains, which arouses a trend that utilizing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to fuse an HR-HSI in an unsupervised manner. Considering the fact that most existing methods are restricted by using linear spectral unmixing, we propose a nonlinear variational probabilistic generative model (NVPGM) for the unsupervised fusion task based on nonlinear unmixing. We model the joint full likelihood of the observed pixels in an LR-HSI and an HR-MSI, both of which are assumed to be generated from the corresponding latent representations, i.e., the abundance vectors. The sufficient statistics of the generative conditional distributions are nonlinear functions with respect to the latent variable, realized by neural networks, which results in a nonlinear spectral mixture model. For scalability and efficiency, we construct two recognition models to infer the latent representations, which are parameterized by neural networks as well. Simultaneously inferring the latent representations and optimizing the parameters are achieved using stochastic gradient variational inference, after which the target HR-HSI is retrieved via feedforward mapping. Though without supervised information about the HR-HSI, NVPGM still can be trained based on extra LR-HSI and HR-MSI data sets in advance unsupervisedly and processes the images at the test phase in real time. Three commonly used data sets are used to evaluate the effectiveness and efficiency of NVPGM, illustrating the outperformance of NVPGM in the unsupervised LR-HSI and HR-MSI fusion task.

关键词： Hyperspectral imaging Spatial resolution probabilistic logic Task analysis Sensors Hyperspectral image (HSI) multispectral image (MSI) nonlinear fusion probabilistic generative model super-resolution

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Hippocampal formation-inspired probabilistic generative model

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NEURAL NETWORKS 2022年第0期151卷 317-335页

作者： Taniguchi, Akira Fukawa, Ayako Yamakawa, Hiroshi Ritsumeikan Univ 1-1-1 Noji Higashi Kusatsu Shiga 5258577 Japan Whole Brain Architecture Initiat Edogawa Ku Nishikoiwa 2-19-21 Tokyo 1330057 Japan Univ Tokyo Bunkyo Ku 7-3-1 Hongo Tokyo 1130033 Japan RIKEN 6-2-3 Furuedai Suita Osaka 5650874 Japan

In building artificial intelligence (AI) agents, referring to how brains function in real environments can accelerate development by reducing the design space. In this study, we propose a probabilistic generative model (PGM) for navigation in uncertain environments by integrating the neuroscientific knowledge of hippocampal formation (HF) and the engineering knowledge in robotics and AI, namely, simultaneous localization and mapping (SLAM). We follow the approach of brain reference architecture (BRA) (Yamakawa, 2021) to compose the PGM and outline how to verify the model. To this end, we survey and discuss the relationship between the HF findings and SLAM models. The proposed hippocampal formation-inspired probabilistic generative model (HF-PGM) is designed to be highly consistent with the anatomical structure and functions of the HF. By referencing the brain, we elaborate on the importance of integration of egocentric/allocentric information from the entorhinal cortex to the hippocampus and the use of discrete-event queues. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Brain-inspired artificial intelligence Brain reference architecture Hippocampal formation Simultaneous localization and mapping probabilistic generative model Phase precession queue assumption

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A whole brain probabilistic generative model: Toward realizing cognitive architectures for developmental robots

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NEURAL NETWORKS 2022年第0期150卷 293-312页

作者： Taniguchi, Tadahiro Yamakawa, Hiroshi Nagai, Takayuki Doya, Kenji Sakagami, Masamichi Suzuki, Masahiro Nakamura, Tomoaki Taniguchi, Akira Ritsumeikan Univ 1-1-1 Noji Higashi Kusatsu Japan Univ Tokyo Bunkyo Ku 7-3-1 Hongo Tokyo Japan Osaka Univ 1-3 Machikane Yama Toyonaka Osaka Japan Grad Univ Okinawa Inst Sci & Technol 1919-1 Tancha Kunigami Okinawa Japan Tamagawa Univ 6-1-1 Tamagawa Gakuen Tokyo Japan Univ Electrocommun 1-5-1 Chofugaoka Tokyo Japan Whole Brain Architecture Initiat Edogawa Ku 2-19-21 Nishikoiwa Tokyo Japan RIKEN 6-2-3 Furuedai Suita Osaka Japan

Building a human-like integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model (PGM)-based cognitive architecture to develop a cognitive system for developmental robots by integrating PGMs. The proposed development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information.& nbsp;In this paper, we describe the rationale for WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, WB-PGM provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics. (C)& nbsp;2022 The Author(s). Published by Elsevier Ltd.

关键词： Cognitive architecture probabilistic generative model Brain-inspired artificial intelligence Artificial general intelligence Developmental robotics

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probabilistic generative model for Hyperspectral Unmixing Accounting for Endmember Variability

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Shi, Shuaikai Zhao, Min Zhang, Lijun Altmann, Yoann Chen, Jie Northwestern Polytech Univ Sch Marine Sci & Technol Ctr Intelligent Acoust & Immers Commun Xian 710072 Peoples R China Minist Ind & Informat Technol Key Lab Ocean Acoust & Sensing Xian 710072 Peoples R China Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland

The complex nature of hyperspectral images makes the analysis of spectral signatures a challenging task in remote sensing. For quantitative analysis, spectral unmixing is a well-established and effective tool to analyze the spectra and spatial distribution of substances in the scene. The classical unmixing algorithms usually fail to tackle spectral variability caused by variations in environmental conditions. Many variants based on the linear mixing process have been proposed to tackle this problem;however, the spectral variability modeling capacity of these algorithms is usually insufficient. In this article, we present a probabilistic generative model to address endmember variability and provide more accurate abundance and endmember estimates. The proposed model simultaneously extracts the endmembers and estimates abundances in an unsupervised manner. In particular, it allows fitting arbitrary endmember distributions through the nonlinear modeling capability of neural networks compared to other methods that use parametric endmember variability models. The performance of the proposed approach is evaluated on both synthetic and real datasets. Experimental results show its superiority in comparison with other state-of-the-art methods. The code of this work is available at https://***/shuaikaishi/PGMSU for the sake of reproducibility.

关键词： Atmospheric modeling probabilistic logic Data models Hyperspectral imaging Libraries Covariance matrices Perturbation methods Endmember variability hyperspectral unmixing probabilistic generative model variational inference (VI)

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A probabilistic generative model to discover the treatments of coexisting diseases with missing data

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 243卷 107870-107870页

作者： Zaballa, Onintze Perez, Aritz Gomez-Inhiesto, Elisa Acaiturri-Ayesta, Teresa Lozano, Jose A. BCAM Basque Ctr Appl Math Bilbao 48009 Bizkaia Spain Hosp Univ Cruces Baracaldo 48903 Bizkaia Spain Univ Basque Country UPV EHU Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence Donostia San Sebastian 20018 Gipuzkoa Spain

Background and Objective: Comorbidities, defined as the presence of co-existing diseases, progress through complex temporal patterns among patients. Learning such dynamics from electronic health records is crucial for understanding the coevolution of diseases. In general, medical records are represented through temporal sequences of clinical variables together with their diagnosis. However, we consider the specific problem where most of the diagnoses are missing. We present a novel probabilistic generative model with a three-foldobjective: (i) identify and segment the medical history of patients into treatments associated with comorbidities;(ii) learn the model associated with each identified disease treatment;and (iii) discover subtypes of patients with similar coevolution of comorbidities. Methods: To this end, the model considers a latent structure for the sequences, where patients are modeled by a latent class defined by the evolution of their comorbidities, and each observed medical event of their clinical history is associated with a latent disease. The learning process is performed using an Expectation-Maximization algorithm that considers the exponential number of configurations of the latent variables and is efficiently solved with dynamic ***: The evaluation of the method is carried out both on synthetic and real world data: the experiments on synthetic data show that the learning procedure allows the generative model underlying the data to be recovered;the experiments on real medical data show accurate results in the segmentation of sequences into different treatments, subtyping of patients and diagnosis ***: We present an interpretable generative model that handles the incompleteness of EHRs and describes the different joint evolution of coexisting diseases depending on the active comorbidities of the patient at each moment.

关键词： Comorbidity modeling Electronic health records Latent variable model Markov model probabilistic generative model

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Brain-inspired probabilistic generative model for double articulation analysis of spoken language

Brain-inspired probabilistic generative model for double art...

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IEEE International Conference on Development and Learning (ICDL)

作者： Taniguchi, Akira Muro, Maoko Yamakawa, Hiroshi Taniguchi, Tadahiro Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu Shiga Japan Ritsumeikan Univ Grad Sch Informat Sci & Engn Kusatsu Shiga Japan Univ Tokyo RIKEN Whole Brain Architecture Initiat Taito Ku Tokyo Japan

ISBN: (数字)9781665413114

ISBN: (纸本)9781665413114

The human brain, among its several functions, analyzes the double articulation structure in spoken language, i.e., double articulation analysis (DAA). A hierarchical structure in which words are connected to form a sentence and words are composed of phonemes or syllables is called a double articulation structure. Where and how DAA is performed in the human brain has not been established, although some insights have been obtained. In addition, existing computational models based on a probabilistic generative model (PGM) do not incorporate neuroscientific findings, and their consistency with the brain has not been previously discussed. This study compared, mapped, and integrated these existing computational models with neuroscientific findings to bridge this gap, and the findings are relevant for future applications and further research. This study proposes a PGM for a DAA hypothesis that can be realized in the brain based on the outcomes of several neuroscientific surveys. The study involved (i) investigation and organization of anatomical structures related to spoken language processing, and (ii) design of a PGM that matches the anatomy and functions of the region of interest. Therefore, this study provides novel insights that will be foundational to further exploring DAA in the brain.

关键词： brain reference architecture double articulation analysis probabilistic generative model spoken language processing structure-constrained interface decomposition

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Learning the progression patterns of treatments using a probabilistic generative model

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JOURNAL OF BIOMEDICAL INFORMATICS 2023年 137卷 104271页

作者： Zaballa, Onintze Perez, Aritz Gomez Inhiesto, Elisa Ayesta, Teresa Acaiturri Lozano, Jose A. BCAM Basque Ctr Appl Math Bilbao 48009 Spain Hosp Univ Cruces Baracaldo 48903 Spain Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp Donostia San Sebastian 20018 Spain

modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model of treatments that are described in terms of sequences of medical activities of variable length. The main objective is to identify distinct subtypes of treatments for a given disease, and discover their development and progression. To this end, the model considers that a sequence of actions has an associated hierarchical structure of latent variables that both classifies the sequences based on their evolution over time, and segments the sequences into different progression stages. The learning procedure of the model is performed with the Expectation-Maximization algorithm which considers the exponential number of configurations of the latent variables and is efficiently solved with a method based on dynamic programming. The evaluation of the model is twofold: first, we use synthetic data to demonstrate that the learning procedure allows the generative model underlying the data to be recovered;we then further assess the potential of our model to provide treatment classification and staging information in real-world data. Our model can be seen as a tool for classification, simulation, data augmentation and missing data imputation.

关键词： Disease progression modeling Electronic health records Markov model probabilistic generative model Unsupervised machine learning

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Conditional Prior probabilistic generative model With Similarity Measurement for ISAR Imaging

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Du, Chuan Xie, Pengfei Zhang, Lei Ma, Yan Tian, Long Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 510275 Peoples R China Beijing Inst Tracking Telemenntry & Telecommun Beijing 100094 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710126 Peoples R China

The higher bandwidth inverse synthetic aperture radar (ISAR) can obtain the higher resolution radar images, which can provide more target information and help improve radar target detection and recognition. It is essential to study how to achieve a precise high-resolution (HR) ISAR image utilizing limited measurement echoes. The existing neural-network-based ISAR imaging methods extract features only from limited measurement echoes, and the common features in HR ISAR images are not utilized sufficiently, which limits the imaging performance improvement. Moreover, in their loss functions, there are no explicit constraints on the correct recovery of strong scattering points, which are important in reflecting the target characteristics. In this letter, we propose a conditional probabilistic generative model to achieve the HR ISAR imaging. By optimizing the well-designed Kullback-Leibler (KL) divergence between conditional prior and approximate posterior probability distribution in the loss function, the common features contained in training HR radar images can be learned, and a suitable prior probability distribution for the latent variable can be obtained. To accurately recover the positions and relative amplitudes of strong scattering points, we blend a similarity measurement that is sensitive to the large values' locations in a vector with the adversarial loss. Both visual and numerical results of extensive experiments prove that the proposed model can obtain enhanced effectiveness and efficiency compared with some counterparts.

关键词： Radar imaging Imaging Scattering Training Radar measurements probabilistic logic Loss measurement Conditional prior generative adversarial networks (GANs) high-resolution (HR) inverse synthetic aperture radar (ISAR) imaging probabilistic generative model similarity measurement variational auto-encoders (VAEs)

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Learning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition

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PATTERN RECOGNITION 2018年 81卷 545-561页

作者： Liu, Li Wang, Shu Hu, Bin Qiong, Qingyu Wen, Junhao Rosenblum, David S. Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Sch Software Engn 174 Shazhengjie Chongqing 400044 Peoples R China Southwest Univ Fac Mat & Energy Chongqing 400715 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Natl Univ Singapore Sch Comp Singapore 117417 Singapore

Complex activity recognition is challenging due to the inherent uncertainty and diversity of performing a complex activity. Normally, each instance of a complex activity has its own configuration of atomic actions and their temporal dependencies. In our previous work, we proposed an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations. However, a major limitation of our previous models is their fixed network structures, which may lead to an overtrained or undertrained model owing to unnecessary or missing links in a network. In this work, we present an improved model that network structures can be automatically learned from empirical data, allowing itself to characterize complex activities with structural varieties. In addition, a new dataset of complex hand activities has been constructed and made publicly available, which is much larger in size than any existing datasets. Empirical evaluations on benchmark datasets as well as our in-house dataset demonstrate the competitiveness of our approach. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Complex activity recognition Structure learning Bayesian network Interval probabilistic generative model American Sign Language dataset

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