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Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo [article]

Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato
2022 arXiv   pre-print
To address these limitations, we present HH-VAEM, a Hierarchical VAE model for mixed-type incomplete data that uses Hamiltonian Monte Carlo with automatic hyper-parameter tuning for improved approximate  ...  Our experiments show that HH-VAEM outperforms existing baselines in the tasks of missing data imputation and supervised learning with missing features.  ...  and from Comunidad de Madrid under grant Y2018/TCS-4705 PRACTICO-CM.  ... 
arXiv:2202.04599v2 fatcat:23j3qtbmkzatnnqf3td72gqvgy

2021 Index IEEE Transactions on Sustainable Energy Vol. 12

2021 IEEE Transactions on Sustainable Energy  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TSTE Oct. 2021 2436-2444 PV Generation Forecasting With Missing Input Data: A Super-Resolution Perception Approach.  ... 
doi:10.1109/tste.2021.3112221 fatcat:evkgacxj2zbvtcdxzg623v7gcm

Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs

Cristian Rotaru, Octav Brudaru
2012 2012 IEEE Congress on Evolutionary Computation  
The approach systematically produces better results than the used basic genetic algorithm and better or similar results with other heuristic methods.  ...  The extensive experimental evaluation uses difficult classical benchmarks and proves the efficiency and the stability of the algorithm.  ...  Gonzalez and Maria Jose del Jesus, A Preliminary Study on Missing Data Imputation in Evolutionary Fuzzy Systems of Subgroup Discovery 301, Edward Hinojosa and Heloisa Camargo, Multiobjective Genetic Generation  ... 
doi:10.1109/cec.2012.6256590 dblp:conf/cec/RotaruB12 fatcat:4ly3nrktw5habc6lf5err7d5py

Learning a Probabilistic Strategy for Computational Imaging Sensor Selection [article]

He Sun, Adrian V. Dalca, Katherine L. Bouman
2020 arXiv   pre-print
The learned probabilistic model is achieved by using a Gibbs sampling inspired network architecture, and is trained end-to-end with a reconstruction network for efficient co-design.  ...  We demonstrate results broadly consistent with expectation, and draw attention to particular structures preferred in the telescope array geometry that can be leveraged to plan future observations and design  ...  ACKNOWLEDGMENTS The authors would like to thank Lindy Blackburn, Alexander Raymond, Michael Johnson, and Sheperd Doeleman for helpful discussions on the constraints of a next-generation EHT array, and  ... 
arXiv:2003.10424v1 fatcat:43gu5rjm45eojhzht2dnmoftcq

a need to know

William A. Spencer, Larry R. Gettman, H. H. Bridge, Allan J. Ryan
1983 Physician and sportsmedicine  
Geometrically Tempered Hamiltonian Monte Carlo-FAkihiko Nishimura, Duke University; David Dunson, Duke University 10:55 a.m.  ...  Data Imputation Methods- Topic-Contributed Based Imputation in Handling Missing Data- FGuanghan Liu, Merck Research Laboratories 10:55 a.m.  ...  SOI continues to pursue high standards of quality and excellence in providing statistics that are relevant, timely, readily accessible, and understandable.  ... 
doi:10.1080/00913847.1983.11708423 pmid:27409254 fatcat:7rlbreoerngb3epmighz7l5vr4

On Fitting Finite Dirichlet Mixture Using ECM and MML [chapter]

Nizar Bouguila, Djemel Ziou
2005 Lecture Notes in Computer Science  
We study a fluid system with single on/off source in which the active (on) and silent periods (off) follow General and Exponential distributions respectively.  ...  Fluid queue models play a vital role in the performance analysis of computer and telecommunication networks.  ...  A natural approach to the problem is through data augmentation (Tanner and Wong 1987, JASA 82:528-550), utilizing Markov chain Monte Carlo (MCMC) techniques that impute fine partitions of the unobserved  ... 
doi:10.1007/11551188_19 fatcat:gtzi5u6emvfyxea3zbklobnvom

Methods for Evaluation of medical prediction Models, Tests And Biomarkers (MEMTAB) 2020 Symposium

2021 Diagnostic and Prognostic Research  
participant data) Big data, electronic health records, dynamic prediction How to quantify overdiagnosis With over 135 delegates and 88 accepted abstracts, we believe we were able to offer a very strong  ...  This year's symposium focussed on the following conference themes: How to develop and apply prediction models and diagnostic tests High-dimensional data and genetic prediction Machine learning for evaluation  ...  We analysed the biomarker data using random effects Poisson and negative binomial models, and for comparison, using a random effects linear regression model.  ... 
doi:10.1186/s41512-021-00094-7 pmid:33789755 fatcat:qpy3vxjtifezzirbuaevywfz3y

Modern applications of machine learning in quantum sciences [article]

Anna Dawid, Julian Arnold, Borja Requena, Alexander Gresch, Marcin Płodzień, Kaelan Donatella, Kim A. Nicoli, Paolo Stornati, Rouven Koch, Miriam Büttner, Robert Okuła, Gorka Muñoz-Gil (+17 others)
2022 arXiv   pre-print
Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.  ...  We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback  ...  Briegel, Lorenzo Cardarelli, Kacper Cybiński, and Mario Krenn for useful discussions and Fesido Studio Graficzne for the graphical design of the Lecture Notes.  ... 
arXiv:2204.04198v2 fatcat:slojwtqwfzgbfgvz3pssdkwhtm

27th Annual Computational Neuroscience Meeting (CNS*2018): Part One

2018 BMC Neuroscience  
All network simulations carried out with NEST (http:// www.nest-simul ator.org).  ...  Networking Fund of the Helmholtz Association and the Helmholtz Portfolio theme "Supercomputing and Modeling for the Human Brain" and the European Union Seventh Framework Programme (FP7/2007-2013) under  ...  For example, some methods use Markov chain Monte Carlo methods to initialize, and impose strong assumptions, such as that spiking follows a Poisson process.  ... 
doi:10.1186/s12868-018-0452-x pmid:30373544 pmcid:PMC6205781 fatcat:xv7pgbp76zbdfksl545xof2vzy

The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science

Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford, Joshua B. Tenenbaum
2011 Behavioral and Brain Sciences  
Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally  ...  Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition.  ...  In these cases, sophisticated approximation schemes are used, such as Markov-chain Monte Carlo (MCMC) or particle filtering (i.e., sequential Monte Carlo).  ... 
doi:10.1017/s0140525x11000239 fatcat:spa6vwghifdfjonvjffbebdv5i

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

Matt Jones, Bradley C. Love
2011 Behavioral and Brain Sciences  
Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally  ...  Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition.  ...  In these cases, sophisticated approximation schemes are used, such as Markov-chain Monte Carlo (MCMC) or particle filtering (i.e., sequential Monte Carlo).  ... 
doi:10.1017/s0140525x10003134 pmid:21864419 fatcat:vdrc6bzpyzfohbzk3j7eagfw5m

Speaker comfort and increase of voice level in lecture rooms

Jonas Brunskog, Anders C. Gade, Gaspar Payà Bellester, Lilian Reig Calbo
2008 Journal of the Acoustical Society of America  
The current work identifies acoustic characteristics of reduced 'flaps' and presents phonetic identification data for continua that manipulate these characteristics.  ...  Degree of intensity dip has the strongest effect, with a minimal dip in intensity more likely to be perceived as 'kneel' and a strong dip in intensity more likely to be perceived as 'needle. ' The results  ...  Chain Monte Carlo approach in order to determine the correct decay model.  ... 
doi:10.1121/1.2934367 fatcat:xr6gp4ldo5bylnxytx2iumrdmi

Fine‐structure processing, frequency selectivity and speech perception in hearing‐impaired listeners

Olaf Strelcyk, Torsten Dau
2008 Journal of the Acoustical Society of America  
The current work identifies acoustic characteristics of reduced 'flaps' and presents phonetic identification data for continua that manipulate these characteristics.  ...  Degree of intensity dip has the strongest effect, with a minimal dip in intensity more likely to be perceived as 'kneel' and a strong dip in intensity more likely to be perceived as 'needle. ' The results  ...  Chain Monte Carlo approach in order to determine the correct decay model.  ... 
doi:10.1121/1.2935148 fatcat:nqyyia5pubamnhqgonegghrudm

Abstracts of Working Papers in Economics

2003 Abstracts of Working Papers in Economics  
Nillesen, Paul AB We propose two version of an address model ~ namely "mill-pricing" and "discriminatory pricing" -with qualityenhancing R&D and spillovers that depend on firms' location.  ...  Extended models, which employ the information from cyclical indicators and factor inputs, however, improve substantially upon the former models on all criteria.  ...  Posterior densities of parameters and smoothed cycles are obtained using Markov chain Monte Carlo methods.  ... 
doi:10.1017/s0951007900006124 fatcat:wdt35iwlavb53fcjgm2ouzuqbu

Abstracts of Working Papers in Economics

2002 Abstracts of Working Papers in Economics  
Using a simple heterogeneous goods trade model of the Armington type and UK data, we show how trade shocks affecting the price of unskilled-intensive goods can be absorbed on the demand side, with little  ...  We argue that this model provides an interpretation for why Britain, Germany, and the U.S. industrialized during the nineteenth century, while the landed aristocracy in Russia and Austria-Hungary blocked  ...  The rank predictions of different models are robust with respect to the hedonic model and the composite commodity definition used in aggregation.  ... 
doi:10.1017/s0951007900005635 fatcat:4go3cqj4ezhdzcps7qsi35kvyi
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