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

共 876 条记录，以下是131-140 订阅

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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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20th international conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

20th International Conference on Information Processing and ...

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20th international conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

ISBN: (纸本)9783031740022

The proceedings contain 33 papers. The special focus in this conference is on Information Processing and Management of Uncertainty in Knowledge-Based Systems. The topics include: graphical Causal models with Discretized Data and Background Information;enhancing the Intelligibility of Boolean Decision Trees with Concise and Reliable probabilistic Explanations;towards an Interpretable Fuzzy Approach to Experimental Design;from Default to Analogical and Paralogical Reasoning. Logics of Pairs and Their Multiple-Valued Extensions;lot Sizing Problem Under Lead-Time Uncertainty;forced Periodic Optimal Scheduling Policy for Graph Reinforcement Learning;similarity Relations Based Numerical Algorithm for Solving Maximin Problems;possibilistic Approach for Meta-analysis;Dual Resource Flexible Job Shop Scheduling Problems: The xBTF Algorithm;information Retrieval Using Fuzzy Fingerprints;Handling Veracity of SVM Predictions;interval Criterion-Based Evidential Set-Valued Classification;analysis of the φ-Index of Inclusion Restricted to a Set of Indexes;enhancing Bayesian Network Structural Learning with Monte Carlo Tree Search;from Quantifying and Propagating Uncertainty to Quantifying and Propagating Both Uncertainty and Reliability: Practice-Motivated Approach to Measurement Planning and Data Processing;extending Idioms for Bayesian Network Construction with Qualitative Constraints;optimizing Causal Interventions in Hybrid Bayesian Networks: A Discretization, Knowledge Compilation, and Heuristic Optimization Approach;visualization of Preference Matrices for Labeled Objects;assessing Causal Graph Uncertainty and Optimized Multivariate Discretization Strategy;exploring Word Embedding in Modeling Risk Perception;similarity of Concepts in Weighted Knowledge Graphs;evidential Linear Regression for Soft Detrended Fluctuation Analysis;COPILS: COmParIson of Linguistic Summaries;groupoids in Categories of Fuzzy Topological Spaces with Continuous Fuzzy Relations;preface;On the Use of Mo

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Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks 10

Two Reformulation Approaches to Maximum-A-Posteriori Inferen...

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10th international conference on probabilistic graphical models (PGM)

作者： Maua, Denis Deratani Ribeiro, Heitor Reis Katague, Gustavo Perez Antonucci, Alessandro Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil Ist Dalle Molle Studi Intelligenza Artificiale Lugano Switzerland

Sum-product networks are expressive efficient probabilistic graphical models that allow for tractable marginal inference. Many tasks however require the computation of maximum-a-posteriori configurations, an NP-hard problem for such models. To date there have been very few proposals for computing maximum-a-posteriori configurations in sum-product networks. This is in sharp difference with other probabilistic frameworks such as Bayesian networks and random Markov fields, where the problem is also NP-hard. In this work we propose two approaches to reformulate maximuma-posteriori inference as other combinatorial optimization problems with widely available solvers. The first approach casts the problem as a similar inference problem in Bayesian networks, overcoming some limitations of previous similar translations. In addition to making available the toolset of maximum-a-posteriori inference on Bayesian networks to sum-product networks, our reformulation also provides further insight into the connections of these two classes of models. The second approach casts the problem as a mixed-integer linear program, for which there exists very efficient solvers. This allows such inferences to be enriched with integer-linear constraints, increasing the expressivity of the models. We compare our reformulation approaches in a large collection of problems, and against state-of-the-art approaches. The results show that reformulation approaches are competitive.

关键词： Tractable probabilistic models Sum-Product Networks probabilistic Circuits Maximuma-Posteriori Inference Mixed-Integer Linear Programming Bayesian Networks

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11th international Advanced Computing conference, IACC 2021

11th International Advanced Computing Conference, IACC 2021

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11th international Advanced Computing conference, IACC 2021

ISBN: (纸本)9783030955014

The proceedings contain 51 papers. The special focus in this conference is on Advanced Computing. The topics include: An Analysis of the Psychological Implications of COVID-19 Pandemic on Undergraduate Students and Efforts on Mitigation;network-Based Identification of Module Biomarker Associated with Hepatocellular Carcinoma;identifying Hub Nodes and Sub-networks from Cattle Rumen Microbiome Multilayer Networks;machine Learning-Based Psychology: A Study to Understand Cognitive Decision-Making;predicting Stock Market Prices Using Sentiment Analysis of News Articles;Sentimental Analysis on Multi-domain Sentiment Dataset Using SVM and Naive Bayes Algorithm;artificial Intelligence in Online Stores’ Processes;early Warning Indicators for Financial Crisis During Covid-19;facial Recognition Based Attendance Monitoring System;Covid Alert System: A Smart Security System to Alert Violations of Covid Protocol Using OpenCV;transfer Learning Using Variational Quantum Circuit;gait Learning Using Reinforcement Learning;data Science in the Business Environment: Architecture, Process and Tools;a Logarithmic Distance-Based Multi-Objective Genetic Programming Approach for Classification of Imbalanced Data;multiview Classification with Missing-Views Through Adversarial Representation and Inductive Transfer Learning;Deep Reinforcement Learning Based Throughput Maximization Scheme for D2D Users Underlaying NOMA-Enabled Cellular Network;An Intrusion Detection System for Blackhole Attack Detection and Isolation in RPL Based IoT Using ANN;evaluating the Efficacy of Different Neural Network Deep Reinforcement Algorithms in Complex Search-and-Retrieve Virtual Simulations;post-hoc Explainable Reinforcement Learning Using probabilistic graphical models;ghostbusters: How the Absence of Class Pairs in Multi-Class Multi-Label Datasets Impacts Classifier Accuracy;Corona Virus Detection Using EfficientNet from CT Scans;reLearner: A Reinforcement Learning-Based Self Driving Car Model Using Gym Enviro

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Hawkesian graphical Event models 10

Hawkesian Graphical Event Models

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10th international conference on probabilistic graphical models (PGM)

作者： Yu, Xiufan Shanmugam, Karthikeyan Bhattacharjya, Debarun Gao, Tian Subramanian, Dharmashankar Xue, Lingzhou Penn State Univ University Pk PA 16802 USA IBM Thomas J Watson Res Ctr Yorktown Hts NY USA

graphical event models (GEMs) provide a framework for graphical representation of multivariate point processes. We propose a class of GEMs named Hawkesian graphical event models (HGEMs) for representing temporal dependencies among different types of events from either a single event stream or multiple independent streams. In our proposed model, the intensity function for an event label is a linear combination of time-shifted kernels where time shifts correspond to prior occurrences of causal event labels in the history, as in a Hawkes process. The number of parameters in our model scales linearly in the number of edges in the graphical model, enabling efficient estimation and inference. This is in contrast to many existing GEMs where the number of parameters scales exponentially in the edges. We use two types of kernels: exponential and Gaussian kernels, and propose a two-step algorithm that combines strengths of both kernels and learns the structure for the underlying graphical model. Experiments on both synthetic and real-world data demonstrate the efficacy of the proposed HGEM, and exhibit expressive power of the two-step learning algorithm in characterizing self-exciting event patterns and reflecting intrinsic Granger-causal relationships.

关键词： Asymptotic consistency Event streams Granger causality graphical event models Hawkes processes Point processes Temporal dependencies

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Constructing gaussian processes for probabilistic graphical models 33

Constructing gaussian processes for probabilistic graphical ...

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33rd international Florida Artificial Intelligence Research Society conference, FLAIRS 2020

作者： Hartwig, Mattis Mohr, Marisa Moller, Ralf University of Lubeck Germany

ISBN: (纸本)9781577358213

probabilistic graphical models have been successfully applied in a lot of different fields, e.g., medical diagnosis and bio-statistics. Multiple specific extensions have been developed to handle, e.g., time-series data or Gaussian distributed random variables. In the case that handles both Gaussian variables and time-series data, downsides are that the models still have a discrete time-scale, evidence needs to be propagated through the graph and the conditional relationships between the variables are bound to be linear. This paper converts two probabilistic graphical models (the Markov chain and the hidden Markov model) into Gaussian processes by constructing covariance and mean functions, that encode the characteristics of the probabilistic graphical models. Our developed Gaussian process based formalism has the advantage of supporting a continuous time scale, direct inference from any time point to the other without propagation of evidence and flexibility to modify the covariance function if needed. © FLAIRS *** right reserved.

关键词： Hidden Markov models

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DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization 23

DGPO: Discovering Multiple Strategies with Diversity-Guided ...

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Proceedings of the 2023 international conference on Autonomous Agents and Multiagent Systems

作者： Wenze Chen Shiyu Huang Yuan Chiang Ting Chen Jun Zhu Tsinghua University Beijing China

ISBN: (纸本)9781450394321

Recent algorithms designed for reinforcement learning tasks focus on finding a single optimal solution. However, in many practical applications, it is important to develop reasonable agents with diverse strategies. In this paper, we propose Diversity-Guided Policy Optimization, an on-policy framework for discovering multiple strategies for the same task. Our algorithm uses diversity objectives to guide a latent code conditioned policy to learn a set of diverse strategies in a single training procedure. Experimental results show that our method efficiently finds diverse strategies in a wide variety of reinforcement learning tasks. We further show that DGPO has similar performance and achieves a higher diversity score or better sample efficiency compared to other baselines.

关键词： deep reinforcement learning diversity probabilistic graphical models

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Data Fusion and State Estimation Using Belief Propagation in Gas Distribution Networks

Data Fusion and State Estimation Using Belief Propagation in...

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international Universities Power Engineering conference (UPEC)

作者： Goekhan Demirel Steven de Jongh Felicitas Mueller Thomas Leibfried Institute of Electrical Energy Systems and High Voltage Technology (IEH) Karlsruhe Institute of Technology Karlsruhe Germany Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

ISBN: (数字)9781665455053

ISBN: (纸本)9781665455060

This paper proposes a solution to the state estimation problem in gas networks using the distributed belief propagation (BP) algorithm. Power system identification applications require precise and robust state estimatiors as well as various sensor information. Compared to augmenting the power system with a very large number of sensors, a limited number of sensors and probabilistic graphical models can be used to infer the system state and reduce hardware investments. A novel BP algorithm propagates the pressure quantities at nodes in the gas network based on pressure manometer signals and applies a correction based on the information of neighboring nodes in the fusion step by using additional supporting sensors. Finally, the data fusion algorithm is demonstrated for a 14-node gas distribution network based on real data. This paper presents a novel algorithm aimed at tackling the traditional weighted least squares method to validate the developed novel approach in order to highlight the advantage of the distributed inference algorithm over traditional methods.

关键词： Power engineering graphical models Data integration Distribution networks probabilistic logic Inference algorithms Power systems

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Evaluating the Effectiveness of Software Development Processes Based on probabilistic Causal models 3

Evaluating the Effectiveness of Software Development Process...

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3rd international conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Nasyrov, Rashit Ufa State Aviation Technology Unvercity Coputer Science and Information Technology Ufa Russia

ISBN: (纸本)9781665439817

An Agile methodology for developing software Agile is considered from the point of view of the effectiveness of practical implementation. An approach to assessing the effectiveness of project activities, presented in the form of generalized scenarios, is described. Such scenarios are built on the idea of a causal relationship between the stages of a scenario. The implementation of the Agile approach in the form of a generalized scenario is considered. A method is proposed for assessing the effectiveness of activities for the implementation of scenarios, which can be considered as generalized design solutions based on invariant criteria. For this purpose, a mathematical apparatus based on Markov chains is used. To implement the methodology, software was implemented using modern tools as part of the Python language, the Qt graphical interface library and the NetworkX graph visualization library. An example of evaluating the effectiveness of the software development process within one sprint within the framework of Agile technology is considered. © 2021 IEEE.

关键词： Python

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Sum-Product Network Decompilation 10

Sum-Product Network Decompilation

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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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