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Beta Probabilistic Databases

Niccolo' Meneghetti, Oliver Kennedy, Wolfgang Gatterbauer
2017 Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17  
We introduce Beta Probabilistic Databases (B-PDBs), a generalization of TI-PDBs designed to support both (i) belief updating and (ii) parameter learning in a principled and scalable way.  ...  We show how this simple expedient enables both belief updating and parameter learning in a principled way, without imposing any burden on regular query processing.  ...  This work was supported by a gift from Oracle, NPS Award #N00244-16-1-0022, and NSF Awards SaTC-1409551 and CAREER IIS-1553547.  ... 
doi:10.1145/3035918.3064026 dblp:conf/sigmod/MeneghettiKG17 fatcat:yc6mvudslzdqdi7ijy5e5vb62u

Simulation of database-valued markov chains using SimSQL

Zhuhua Cai, Zografoula Vagena, Luis Perez, Subramanian Arumugam, Peter J. Haas, Christopher Jermaine
2013 Proceedings of the 2013 international conference on Management of data - SIGMOD '13  
Other key extensions include the ability to explicitly define recursive versions of a stochastic table and the ability to execute the simulation in a MapReduce environment.  ...  We focus on applying SimSQL to Bayesian machine learning.  ...  The order in which to update these three parameters is arbitrary; we choose to first initialize a and b, and then update σ 2 , a and b in sequence.  ... 
doi:10.1145/2463676.2465283 dblp:conf/sigmod/CaiVPAHJ13 fatcat:qk67h3t6erauxeoi7eg2kxmwq4

Multimodal estimation and communication of latent semantic knowledge for robust execution of robot instructions

Jacob Arkin, Daehyung Park, Subhro Roy, Matthew R Walter, Nicholas Roy, Thomas M Howard, Rohan Paul
2020 The international journal of robotics research  
We introduce a probabilistic model that fuses linguistic knowledge with visual and haptic observations into a cumulative belief over latent world attributes to infer the meaning of instructions and execute  ...  A robot's ability to interpret and execute commands is fundamentally tied to its semantic world knowledge.  ...  Toyota Research Institute (TRI) Award Number LP-C000765-SR, and Lockheed Martin Co.. We thank Michael Noseworthy for valuable feedback on this manuscript.  ... 
doi:10.1177/0278364920917755 fatcat:u2w3o7h4svea5gdryo6bfe5xae

Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data

Hamid Mousavi, Mareike Buhl, Enrico Guiraud, Jakob Drefs, Jörg Lücke
2021 Entropy  
This transition from binary to interval data poses a number of challenges including a transition from a Bernoulli to a Beta distribution to model symptom statistics.  ...  To meet the challenges emerging when generalizing from Bernoulli to Beta distributed observables, we investigate a novel LVM that uses a maximum non-linearity to model how the latents determine means and  ...  Acknowledgments: We acknowledge support by the HPC Cluster CARL of Oldenburg University and by the HLRN network of HPC clusters (project nim00006).  ... 
doi:10.3390/e23050552 pmid:33947060 fatcat:nfetcpywavg37kypodcenk2dpq

Improving the quality of K-NN graphs through vector sparsification: application to image databases

Michael E. Houle, Xiguo Ma, Vincent Oria, Jichao Sun
2014 International Journal of Multimedia Information Retrieval  
Given a query object, the similarity between the query object and a database object can be computed as the distance between their feature vectors.  ...  Secondly, in a content-based image retrieval system, a generalized version of the Laplacian Score is used to compute different feature subspaces for images in the database.  ...  Their approach combines a hierarchical beta-Bernoulli prior and a Dirichlet process mixture model. Local or global feature selection can be achieved by adjusting the variance of the beta prior.  ... 
doi:10.1007/s13735-014-0067-7 fatcat:2w3k3fcexjax5i6h42hqqcrtzi

PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming [article]

Alexander K. Lew, Monica Agrawal, David Sontag, Vikash K. Mansinghka
2020 arXiv   pre-print
PClean is powered by three modeling and inference contributions: (1) a non-parametric model of relational database instances, customizable via probabilistic programs, (2) a sequential Monte Carlo inference  ...  Based on this view, we present PClean, a probabilistic programming language for leveraging dataset-specific knowledge to clean and normalize dirty data.  ...  , Josh Tenenbaum, and Veronica Weiner for useful con-  ... 
arXiv:2007.11838v4 fatcat:navjwv7vpfbzhaq4mugkbucvve

A Bayesian approach to discovering truth from conflicting sources for data integration

Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, Jiawei Han
2012 Proceedings of the VLDB Endowment  
In this work, we propose a probabilistic graphical model that can automatically infer true records and source quality without any supervision.  ...  In contrast to previous methods, our principled approach leverages a generative process of two types of errors (false positive and false negative) by modeling two different aspects of source quality.  ...  ACKNOWLEDGEMENTS We thank Ashok Chandra, Duo Zhang, Sahand Negahban and three anonymous reviewers for their valuable comments. The work was supported in part by the U.S.  ... 
doi:10.14778/2168651.2168656 fatcat:z376bflf4za3vaesddqndmweuq

A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration [article]

Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, Jiawei Han
2012 arXiv   pre-print
In this work, we propose a probabilistic graphical model that can automatically infer true records and source quality without any supervision.  ...  In contrast to previous methods, our principled approach leverages a generative process of two types of errors (false positive and false negative) by modeling two different aspects of source quality.  ...  ACKNOWLEDGEMENTS We thank Ashok Chandra, Duo Zhang, Sahand Negahban and three anonymous reviewers for their valuable comments. The work was supported in part by the U.S.  ... 
arXiv:1203.0058v1 fatcat:3pllovnmlzaijdnt2vqjmzf5nu

Uncertainty Aware AI ML: Why and How [article]

Lance Kaplan, Federico Cerutti, Murat Sensoy, Alun Preece, Paul Sullivan
2018 arXiv   pre-print
This paper argues the need for research to realize uncertainty-aware artificial intelligence and machine learning (AI\&ML) systems for decision support by describing a number of motivating scenarios.  ...  A theoretical demonstration illustrates how two emerging uncertainty-aware ML and AI technologies could be integrated and be of value for a route planning operation.  ...  The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.  ... 
arXiv:1809.07882v1 fatcat:uttcg75g6bhbpg3m5wsg7gfl3m

InferSpark: Statistical Inference at Scale [article]

Zhuoyue Zhao, Jialing Pei, Eric Lo, Kenny Q. Zhu, Chris Liu
2017 arXiv   pre-print
It is a perfect time to unite these two great directions to produce a programmable big data analysis framework.  ...  The emergence of probabilistic programming languages has showed the promise of developing sophisticated probabilistic models in a succinct and programmatic way.  ...  The rest is similar to a class definition in scala. The model parameters ("alpha" and "beta") are constants to the model.  ... 
arXiv:1707.02047v2 fatcat:x264bzypyrdtlk3taztdfvgvgu

AXIS

Joseph Jay Williams, Juho Kim, Anna Rafferty, Samuel Maldonado, Krzysztof Z. Gajos, Walter S. Lasecki, Neil Heffernan
2016 Proceedings of the Third (2016) ACM Conference on Learning @ Scale - L@S '16  
AXIS asks learners to generate, revise, and evaluate explanations as they solve a problem, and then uses machine learning to dynamically determine which explanation to present to a future learner, based  ...  Results from a case study deployment and a randomized experiment demonstrate that AXIS elicits and identifies explanations that learners find helpful.  ...  Soliciting explanations from learners addresses the problem of scalable creation of explanations for a large and potentially growing database of activities.  ... 
doi:10.1145/2876034.2876042 dblp:conf/lats/WilliamsKRMGLH16 fatcat:qm5gz4tawzcidkhmmfpebi7lem

BreachRadar: Automatic Detection of Points-of-Compromise [chapter]

Miguel Araujo, Miguel Almeida, Jaime Ferreira, Luis Silva, Pedro Bizarro
2017 Proceedings of the 2017 SIAM International Conference on Data Mining  
Much of this fraud starts with a Point-of-Compromise (a data breach or a skimming operation) where credit and debit card digital information is stolen, resold, and later used to perform fraud.  ...  We implement this method using Apache Spark and show its linear scalability in the number of machines and transactions.  ...  FaBP [12] is a fast approximation to Belief Prop-agation with low sensitivity to input parameters.  ... 
doi:10.1137/1.9781611974973.63 dblp:conf/sdm/AraujoAFSB17 fatcat:ygq5scwkvbda3aqjntz6qrldq4

Index—Volumes 1–89

1997 Artificial Intelligence  
entailed by a -67 1 database systems 533, 623 interacting with -533 research 623 database updates 395 database updating desiderata for -1375 rational -1375 databases 301,686,1 i 14 CAD -1037  ...  1086 probabilistic analysis 179, 252, 1303 of the performance of the game-searching SSS* algorithm 252 probabilistic approach to planning 1223 B* 1340 belief networks 907 conflicts 1397 constructs  ... 
doi:10.1016/s0004-3702(97)80122-1 fatcat:6az7xycuifaerl7kmv7l3x6rpm

Big Learning with Bayesian Methods [article]

Jun Zhu, Jianfei Chen, Wenbo Hu, Bo Zhang
2017 arXiv   pre-print
Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning.  ...  Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems,  ...  ACKNOWLEDGEMENTS The work is supported by National 973 Projects (2013CB329403), NSF of China Projects (61322308, 61332007), and Tsinghua Initiative Scientific Research Program (20121088071).  ... 
arXiv:1411.6370v2 fatcat:zmxse4kkqjgffkricevyumaoiu

Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design [article]

Ruijin Cang, Yaopengxiao Xu, Shaohua Chen, Yongming Liu, Yang Jiao, Max Yi Ren
2017 arXiv   pre-print
In this paper, we develop a feature learning mechanism based on convolutional deep belief network to automate a two-way conversion between microstructures and their lower-dimensional feature representations  ...  , and to achieves a 1000-fold dimension reduction from the microstructure space.  ...  Informally, this line of approaches use sampled microstructures to learn a mapping x = Φ(z) and its inverse z = Φ −1 (x) (the unsupervised learning step), where x is a learned feature representation of  ... 
arXiv:1612.07401v3 fatcat:2ft47cvkfffhjm4lkrz7hz4lzy
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