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检索条件"任意字段=International Conference on Probabilistic Graphical Models"

共 872 条记录，以下是51-60 订阅

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On the Performance of Hierarchical Distributed Correspondence Graphs for Efficient Symbol Grounding of Robot Instructions

On the Performance of Hierarchical Distributed Correspondenc...

引用

IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Chung, Istvan Propp, Oron Waller, Matthew R. Howard, Thomas M. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Toyota Technol Inst Chicago IL 60637 USA Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781479999941

Natural language interfaces are powerful tools that enables humans and robots to convey information without the need for extensive training or complex graphical interfaces. Statistical techniques that employ probabilistic graphical models have proven effective at interpreting symbols that represent commands and observations for robot direction-following and object manipulation. A limitation of these approaches is their inefficiency in dealing with larger and more complex symbolic representations. Herein, we present a model for language understanding that uses parse trees and environment models both to learn the structure of probabilistic graphical models and to perform inference over this learned structure for symbol grounding. This model, called the Hierarchical Distributed Correspondence Graph (HDCG), exploits information about symbols that are expressed in the corpus to construct min-imalist graphical models that are more efficient to search. In a series of comparative experiments, we demonstrate a significant improvement in efficiency without loss in accuracy over contemporary approaches for human-robot interaction.

关键词： Context graphical models Grounding Mathematical model Natural languages probabilistic logic Robots

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Anytime Learning of Sum-Product and Sum-Product-Max Networks 11

Anytime Learning of Sum-Product and Sum-Product-Max Networks

引用

international conference on probabilistic graphical models

作者： Pawar, Swaraj Doshi, Prashant Univ Georgia Sch Comp THINC Lab Athens GA 30602 USA

Prominent algorithms for learning sum-product networks (SPN) and sum-product-max networks (SPMN) focus on learning models from data that deliver good modeling performance without regard to the size of the learned network. Consequently, the learned networks can get very large, which negatively impacts inference time. In this paper, we introduce anytime algorithms for learning SPNs and SPMNs. These algorithms generate intermediate but provably valid models whose performance progressively improves as more time and computational resources are allocated to the learning. They flexibly trade off good model performance with reduced learning time, offering the benefit that SPNs and SPMNs of small sizes (but with reduced likelihoods) can be learned quickly. We comprehensively evaluate the anytime algorithms on two testbeds and demonstrate that the network performance improves with time and reflects the expected performance profile of an anytime algorithm. We expect these anytime algorithms to become the default learning techniques for SPNs and SPMNs given their clear benefit over classical batch learning techniques.

关键词： Anytime algorithms decision making flexible probabilistic graphical models

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Discovering the Temporal Interactions between Clinical Observations 3

Discovering the Temporal Interactions between Clinical Obser...

引用

international conference on Healthcare Informatics (ICHI)

作者： Goodwin, Travis Univ Texas Dallas Human Language Technol Res Inst Dallas TX 75080 USA

ISBN: (纸本)9781467395489

The explosion of clinical information provided by the advent of electronic health records (EHRs) offers an exciting opportunity to substantially improve the quality of health care. Updated throughout each patient's health care, EHRs document a wide variety of clinical observations, such as the patient's diagnoses, risk factors, medications, and test results at various points in the patient's history. This allows for the secondary use of EHRs to provide substantial information about the way that patients' clinical pictures and therapies have evolved over time. In this abstract, we outline our ongoing work towards harnessing the chronological, natural language text in EHRs in order to model how individual patients' clinical pictures and therapies have evolved. Moreover, we show our experiments on improving predictive accuracy by discovering latent groups of "similar" patients.

关键词： graphical models History Informatics Medical treatment Predictive models probabilistic logic disease progression probabilistic graphical models temporal prediction probabilistic logic graphical models Informatics Prediction models MEDICAL TREATMENT Disease Progression Exploration Medicine medical history Patient history Quality of Health Care

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Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound 11

Discovery and density estimation of latent confounders in Ba...

引用

international conference on probabilistic graphical models

作者： Chobtham, Kiattikun Constantinou, Anthony C. Queen Mary Univ London Sch Elect Engn & Comp Sci Bayesian Artificial Intelligence Res Lab London E1 4NS England

Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we focus on both discovering and learning the distribution of latent confounders. This task requires solutions that come from different areas of statistics and machine learning. We combine elements of variational Bayesian methods, expectation-maximisation, hill-climbing search, and structure learning under the assumption of causal insufficiency. We propose two learning strategies;one that maximises model selection accuracy, and another that improves computational efficiency in exchange for minor reductions in accuracy. The former strategy is suitable for small networks and the latter for moderate size networks. Both learning strategies perform well relative to existing solutions.

关键词： Ancestral graphs EM algorithm greedy search hill-climbing search hidden variables probabilistic graphical models variational inference

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Towards Scalable Spatial probabilistic graphical Modeling 19

Towards Scalable Spatial Probabilistic Graphical Modeling

引用

27th ACM SIGSPATIAL international conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Sabek, Ibrahim Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

No abstract available.

ISBN: (纸本)9781450369091

No abstract available.

关键词： Spatial probabilistic graphical models Spatial Analysis Markov Logic Networks Scalability

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Event Recognition in Photo Albums Using probabilistic graphical Model and Feature Relevance 23

Event Recognition in Photo Albums Using Probabilistic Graphi...

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23rd international conference on Pattern Recognition (ICPR)

作者： Bacha, Siham Allili, Mohand Said Benblidia, Nadjia Univ Blida 1 Dept Comp Sci Blida Algeria Univ Quebec Outaouais Dept Comp Sci Gatineau PQ Canada

ISBN: (纸本)9781509048472

The exponential use of digital cameras has raised a new problem: how to store/retrieveimages/albums in very large photo databases that correspond to special events. In this paper, we propose a new probabilistic graphical model (PGM) to recognize events in photo albums stored by users. The PGM combines high-level image features consisting of scenes and objects detected in images. To consider the discriminative power of features, our model integrates the object/scene relevance for more precise prediction of semantic events in photo albums. Experimental results carried out on the challenging PEC dataset with 807 photo albums are presented.

关键词： event recognition feature relevance probabilistic graphical models

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Weighted Ensemble of Neural and probabilistic graphical models for Click Prediction 2021

Weighted Ensemble of Neural and Probabilistic Graphical Mode...

引用

5th international conference on Information System and Data Mining (ICISDM)

作者： Bisht, Kritarth Susan, Seba Delhi Technol Univ Dept Informat Technol Informat Syst Bawana Rd Delhi 110042 India Delhi Technol Univ Dept Informat Technol Bawana Rd Delhi 110042 India

ISBN: (纸本)9781450389549

Predicting user behavior in web mining is an important concept with commercial implications. The user response to search engine results is crucial for understanding the relative popularity of websites and market trends. The most popular way of understanding user interests is via click models that can predict whether a user will click on a search engine result or not, based on past observations. There are two main categories of click models, namely, the neural network based models and the probabilistic graphical models. In this paper, we combine the goodness of both approaches by presenting a weighted ensemble of both types of models. The weighted sum of softmax scores integrates the predictions of the individual models. Assigning higher weights to the neural models is found to improve the performance of the ensemble. The AUC and perplexity scores of our weighted ensemble model are higher than the state of the art, as proved by experiments on the benchmark Tiangong-ST dataset.

关键词： Click prediction model Weighted ensemble Neural model probabilistic graphical models Decision fusion Web-based information retrieval

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Comparison of Different probabilistic graphical models as Causal models in Alarm Flood Reduction 17

Comparison of Different Probabilistic Graphical Models as Ca...

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17th IEEE international conference on Industrial Informatics (INDIN)

作者： Wunderlich, Paul Hranisavljevic, Nana OWL Univ Appl Sci & Arts InT Inst Ind IT Lemgo Germany Fraunhofer IOSB INA Fraunhofer Ctr Machine Learning Lemgo Germany

ISBN: (纸本)9781728129273

The increasing amount of alarms and information for an operator in a modern plant becomes a significant safety risk. Although the notifications are a valuable support, they also lead to the curse of overloading with information for the operator. Due to the huge amount of alarms it is almost impossible to separate the crucial information from the insignificant ones. Therefore, new procedures are required to reduce these alarm floods and support the operator to minimize the safety risk. One approach is based on learning a causal model that represents the relationships between the alarms. This allows alarm sequences that are causally implied to be reduced to the root cause alarm. Fundamental element of this approach is the causal model. Therefore in this work, different probabilistic graphical models are considered and evaluated on the basis of appropriate criteria. A real use case of a bottle filling module serves as a benchmark for how well they are suitable as a causal model for the application in alarm flood reduction.

关键词： probabilistic graphical model causal model alarm flood reduction Bayesian network Markov chain restricted boltzmann machine automata

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Discriminative Non-Parametric Learning of Arithmetic Circuits 10

Discriminative Non-Parametric Learning of Arithmetic Circuit...

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10th international conference on probabilistic graphical models (PGM)

作者： Ramanan, Nandini Das, Mayukh Kersting, Kristian Natarajan, Sriraam

Arithmetic Circuits (AC) and Sum-Product Networks (SPN) have recently gained significant interest by virtue of being tractable deep probabilistic models. We propose the first gradient-boosted method for structure learning of discriminative ACs (DACs), called DACBOOST. In discrete domains ACs are essentially equivalent to mixtures of trees, thus DACBOOST decomposes a large AC into smaller tree-structured ACs and learns them in sequential, additive manner. The resulting non-parametric manner of learning DACs results in a model with very few tuning parameters making our learned model significantly more efficient. We demonstrate on standard data sets and real data sets, efficiency of DACBOOST compared to state-of-the-art DAC learners without sacrificing effectiveness.

关键词： probabilistic Machine Learning Ensemble Learning Tractable probabilistic models Structure Learning

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Revising Markov boundary for multiagent probabilistic inference

Revising Markov boundary for multiagent probabilistic infere...

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international conference on Intelligent Agent Technology (IAT 2004)

作者： An, XD Xiang, Y Cercone, N Univ Waterloo Sch Comp Sci Waterloo ON N2L 3G1 Canada

ISBN: (纸本)0769521010

Multiply sectioned Bayesian networks (MSBNs) extend Bayesian networks (BNs) to graphical models that provide a coherent framework for probabilistic inference in cooperative multiagent distributed interpretation systems. Observation plays an important role in the inference with graphical models. Since observation of each observable variable has a cost, it would be helpful if we can find the most relevant variables to observe. In a probabilistic model, a Markov boundary of a variable provides a minimal set of variables that shields the variable from the influence of all other variables. However the concept cannot be used directly for observation. First, it is generally intractable to verify conditional independencies in a probabilistic model. Second, the Markov boundary members may not be observable. Third, it is defined only for a single variable. Finally, it is not unique. By revising the concept to address these issues, we introduce the concept of observable Markov boundary of a set of nodes defined on d-separation of graphical models. The observable Markov boundary captures all relevant variables to observe for probabilistic inference with graphical models. In an MSBN, the observable Markov boundary of a set of nodes may span across all Bayesian subnets. We present an algorithm for cooperative computation of the observable Markov boundary of a set of nodes in an MSBN without revealing subnet structures.

关键词： Multi agent systems

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