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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81-90 订阅

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Hierarchical graphical models for Context-Aware Hybrid Brain-Machine Interfaces 40

Hierarchical Graphical Models for Context-Aware Hybrid Brain...

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40th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Ozdenizci, Ozan Gunay, Sezen Yagmur Quivira, Fernando Erdogmus, Deniz Northeastern Univ Dept Elect & Comp Engn Cognit Syst Lab Boston MA 02115 USA

ISBN: (纸本)9781538636466

We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities. Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses. Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics.

关键词： hybrid BMIs EEG EMG neural prosthetics hierarchical graphical models probabilistic inference

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PGM PyLib: A Toolkit for probabilistic graphical models in Python 10

PGM PyLib: A Toolkit for Probabilistic Graphical Models in P...

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10th international conference on probabilistic graphical models, PGM 2020

作者： Serrano-Pérez, Jonathan Sucar, L. Enrique Instituto Nacional de Astrofísica Óptica y Electrónica Puebla Mexico

PGM PyLib is a toolkit that contains a wide range of probabilistic graphical models algorithms implemented in Python, and serves as a companion of the book probabilistic graphical models: Principles and Applications. Currently, the algorithms implemented include: Bayesian classifiers, hidden Markov models, Markov random fields, and Bayesian networks;as well as some general functions. The toolkit is open source, can be downloaded from: https://***/jona2510/PGM_PyLib . © 2020 Proceedings of Machine Learning Research. All rights reserved.

关键词： Python

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Sum-Product Network Decompilation 10

Sum-Product Network Decompilation

引用

10th international conference on probabilistic graphical models (PGM)

作者： Butz, Cory J. Oliveira, Jhonatan S. Peharz, Robert Univ Regina Regina SK Canada Eindhoven Univ Technol Eindhoven Netherlands Univ Cambridge Cambridge England

There exists a dichotomy between classical probabilistic graphical models, such as Bayesian networks (BNs), and modern tractable models, such as sum-product networks (SPNs). The former generally have intractable inference, but provide a high level of interpretability, while the latter admit a wide range of tractable inference routines, but are typically harder to interpret. Due to this dichotomy, tools to convert between BNs and SPNs are desirable. While one direction - compiling BNs into SPNs - is well discussed in Darwiche's seminal work on arithmetic circuit compilation, the converse direction - decompiling SPNs into BNs - has received surprisingly little attention. In this paper, we fill this gap by proposing SPN2BN, an algorithm that decompiles an SPN into a BN. SPN2BN has several salient features when compared to the only other two works decompiling SPNs. Most significantly, the BNs returned by SPN2BN are minimal independence-maps that are more parsimonious with respect to the introduction of latent variables. Secondly, the output BN produced by SPN2BN can be precisely characterized with respect to a compiled BN. More specifically, a certain set of directed edges will be added to the input BN, giving what we will call the moral-closure. Lastly, it is established that our compilation-decompilation process is idempotent. This has practical significance as it limits the size of the decompiled SPN.

关键词： probabilistic graphical models Sum-product networks Bayesian networks

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Hybrid Correlational graphical models for Reasoning in Detecting Systems

Hybrid Correlational Graphical Models for Reasoning in Detec...

引用

IEEE 24th international conference on Tools with Artificial Intelligence (ICTAI)

作者： Shi, Dongyu Xu, Sufang E China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China

ISBN: (纸本)9780769549156;9781479902279

Using probabilistic graphical models to deal with uncertainties by modeling relationships among detecting objects is a common method for event detecting systems. However, not all relations are captured accurately by former graphical models. This paper presents a hybrid correlational model for typical abnormal event detecting systems that have correlated objects. It captures the OR relation of multiple influences from different sources of the abnormal event. An algorithm based on message passing is developed for efficient reasoning in the model. Analysis and experiments are provided to compare it with former graphical modeling by results on the detecting objects that lack of local evidence, and by their sensitivity to the occurrence of abnormal event.

关键词： inference mechanisms object detection probability

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Automatic Annotation Extension and Classification of Documents Using a probabilistic graphical Model 13

Automatic Annotation Extension and Classification of Documen...

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13th IAPR international conference on Document Analysis and Recognition (ICDAR)

作者： Bouzaieni, Abdessalem Barrat, Sabine Tabbone, Salvatore Univ Lorraine LORIA XILOPIX Vandoeuvre Les Nancy France Univ Tours Comp Sci Lab Tours France Univ Lorraine LORIA Vandoeuvre Les Nancy France

ISBN: (纸本)9781479918058

With the fast growth of document images, document annotation has become a research area of great interest. Annotation allows to describe the semantic content of documents and facilitates their use and research. However, for a huge number of documents, the manual annotation of each document becomes a tedious task. A solution is to annotate a small part of the documents and to extend it automatically to the whole dataset. In this paper, we propose a model for annotation extension and document classification using a probabilistic graphical model. In this latter, we combine visual and textual characteristics and we show that the integration of the user feedback improves the annotation step.

关键词： document image processing image classification probability automatic document annotation extension automatic document classification document annotation document images probabilistic graphical model textual characteristics user feedback visual characteristics Manganese Manuals Presses user feedback document image processing Image classification ANNOTATIONS probabilistic Model manganese document image graphical models press-in auto-marking

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A Framework Based on graphical models with Logic for Chinese Named Entity Recognition 3

A Framework Based on Graphical Models with Logic for Chinese...

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3rd international Joint conference on Natural Language Processing, IJCNLP 2008

作者： Yu, Xiaofeng Lam, Wai Chan, Shing-Kit Information Systems Laboratory Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong

Chinese named entity recognition (NER) has recently been viewed as a classification or sequence labeling problem, and many approaches have been proposed. However, they tend to address this problem without considering linguistic information in Chinese NEs. We propose a new framework based on probabilistic graphical models with firstorder logic for Chinese NER. First, we use Conditional Random Fields (CRFs), a standard and theoretically well-founded machine learning method based on undirected graphical models as a base system. Second, we introduce various types of domain knowledge into Markov Logic Networks (MLNs), an effective combination of first-order logic and probabilistic graphical models for validation and error correction of entities. Experimental results show that our framework of probabilistic graphical models with first-order logic significantly outperforms the state-of-the-art models for solving this task. © 2008 IJCNLP 2008 - 3rd international Joint conference on Natural Language Processing, Proceedings of the conference. All rights reserved.

关键词： Error correction

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Joint Entity Recognition and Linking in Technical Domains Using Undirected probabilistic graphical models 1

引用

1st international conference on Language, Data and Knowledge (LDK)

作者： ter Horst, Hendrik Hartung, Matthias Cimiano, Philipp Bielefeld Univ Semant Comp Grp Cognit Interact Technol Cluster Excellence CITEC Bielefeld Germany

ISBN: (数字)9783319598888

ISBN: (纸本)9783319598888;9783319598871

The problems of recognizing mentions of entities in texts and linking them to unique knowledge base identifiers have received considerable attention in recent years. In this paper we present a probabilistic system based on undirected graphical models that jointly addresses both the entity recognition and the linking task. Our framework considers the span of mentions of entities as well as the corresponding knowledge base identifier as random variables and models the joint assignment using a factorized distribution. We show that our approach can be easily applied to different technical domains by merely exchanging the underlying ontology. On the task of recognizing and linking disease names, we show that our approach outperforms the state-of-the-art systems DNorm and TaggerOne, as well as two strong lexicon-based baselines. On the task of recognizing and linking chemical names, our system achieves comparable performance to the state-of-the-art.

关键词： Joint entity recognition and linking Undirected probabilistic graphical models Diseases Chemicals

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Protein Secondary Structure Prediction using Large Margin Methods

Protein Secondary Structure Prediction using Large Margin Me...

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8th IEEE/ACIS international conference on Computer and Information Science

作者： Tang, Buzhou Wang, Xuan Wang, Xiaolong Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

ISBN: (纸本)9780769536415

Protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve prediction performance. In this paper, we propose a new large margin approach for protein secondary structure prediction, which consider the problem as a sequence labeling problem like probabilistic graphical models. It doesn't only make full use of the correlation between neighboring secondary structures like graphical chain models, but also shares the key advantages of other SVM-based methods, i.e. learming non-linear discriminant via kernel functions. The experimental results on datasets: CB513 and RS126 show that our algorithm outperforms other state-of-the-art methods.

关键词： Protein secondary structure prediction large margin approach sequence labeling problem probabilistic graphical models

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probabilistic Model-Building Genetic Algorithms 12

Probabilistic Model-Building Genetic Algorithms

引用

14th international conference on Genetic and Evolutionary Computation conference (GECCO)

作者： Pelikan, Martin Univ Missouri Dept Math & Comp Sci MEDAL St Louis MO 63121 USA

ISBN: (纸本)9781450311786

probabilistic model-building genetic algorithms (PMBGAs), also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs), guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Building and sampling probabilistic models of promising solutions enables the use of machine learning techniques for automated discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. However, EDAs are not only optimization techniques; besides the optimum or its approximation, EDAs provide practitioners with a series of probabilistic models that reveal a lot of information about the problem being solved. This information can in turn be used to design problem-specific neighborhood operators for local search, to bias future runs of EDAs on a similar problem, or to create an efficient computational model of the problem. The tutorial probabilistic Model-Building GAs provides an introduction to PMBGAs with an overview of major research directions in this area.

关键词： scalable optimization probabilistic model-building genetic algorithms iterated density-estimation algorithms metaheuristics optimization graphical models machine learning evolutionary computation estimation of distribution algorithms

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Learning the Features ignored by Classification models using Staged Modeling Variational Autoencoder

Learning the Features ignored by Classification Models using...

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international Joint conference on Neural Networks (IJCNN)

作者： Ruan, Zhihui Zhang, Jianwei South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9780738133669

Classification networks usually only save the specified factor associated with labels. We propose a probabilistic graphical model (PGM) to learn the features ignored by arbitrary classification networks using staged modeling. To implement the staged PGM, we introduce Staged Modeling Variational Autoencoder (SMVAE), in which the first stage can apply arbitrary classification models to encode the specified factor, then optimizing the Evidence Lower Bound (ELBO) given optimal specified factor to compress the features ignored at the first stage into the unspecified factor. Besides, SMVAE can learn the disentangled unspecified factors unsupervised by further decomposing the ELBO given the optimal specified factor. At last, we introduce Adain based on Infusion Training in SMVAE to learn more details to reconstruct data. Detailed experiments are given to evaluate the disentanglement and performance of SMVAE.

关键词： Training graphical models Neural networks probabilistic logic

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