[PDF] Top 20 Neural Autoregressive Distribution Estimation
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Neural Autoregressive Distribution Estimation
... present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied to the problem of unsupervised distribution and density ...the ... See full document
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Document Neural Autoregressive Distribution Estimation
... feed-forward neural networks for learning the distribution over textual ...the Neural Autoregressive Distribution Esti- mator (NADE) model which has been shown to be a good estimator of ... See full document
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Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks
... Calibration of CNNs is important as predictions need to be equally accurate and confident. Calibration and uncertainty estimation of predictors has been an ongoing interest to the machine learning community [12, ... See full document
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Adaptation of multiple regression analysis to identify effective factors of water losses in water distribution systems
... Artificial Neural Network (ANN) and Principle Component Analysis ...NRW estimation using ...NRW estimation using ANN. The detailed NRW estimation methodology presented through the study, as a ... See full document
8
Predicting Turbidity in Water Distribution Trunk Mains Using Nonlinear Autoregressive Exogenous Artificial Neural Networks
... Artificial Neural Networks (ANN), as an alternative to hydraulic models, may be able to deal with the complex nature of the problem and be applied to different system conditions where sufficient data is available ... See full document
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Document Informed Neural Autoregressive Topic Models with Distributional Prior
... in neural autoregressive topic model by incorporating full contextual information around words in a document to boost the likelihood of each word (and ...Informed Neural Autore- gressive ... See full document
8
Neural network modeling and geochemical water analyses to understand and forecast karst and non-karst part of flash floods (case study on the Lez river, (Southern France)
... As the proposed methodology suffers from several approx- imations and hypothesis due to the lack of measurements and knowledge about the hydrosystem, we thought necessary to present the limitations of this work. First, ... See full document
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A product autoregressive model with log-Laplace marginal distribution
... The autoregressive models associated with the exponential, gamma and mixed exponential distributions are introduced by Lawrance ...innovation distribution can be generated easily as a compound Poisson ... See full document
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Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach
... series estimation and prediction, the most common models which were typically and frequently employed are autoregressive conditional heteroskedasticity or so-called the ARCH model, (Baum ...the ... See full document
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Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models
... Further, we contribute to the literature by putting forward two test statistics to test for pa- rameter constancy in our model. These model specification tests allow us to discriminate between a standard linear spatial ... See full document
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Maximum likelihood estimation for directional conditionally autoregressive models
... δ 1 and δ 2 are good approximations to finite sample Monte Carlo standard errors (MCSE’s) when the true values are positive. For all these boundary values of δ , the nominal 95% coverage probability (CP) is relatively ... See full document
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Estimation and forecasting in vector autoregressive moving average models for rich datasets
... We establish the consistency and asymptotic distribution for the IOLS estimator by considering the general weak and strong VARMA(p,q) models. It is also important to point out that our theoretical results are ... See full document
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Cross validation aggregation for combining autoregressive neural network forecasts
... and neural networks (Wolff 1987; Clarida et ...unbiased estimation is known to fail when the number of models grows exponentially with the number of ...error estimation rather than forecast ... See full document
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COMPARATIVE ANALYSIS OF THE PERFORMANCE OF ARTIFICIAL NEURAL NETWORKS (ANNs) AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS ON RAINFALL FORECASTING
... Delay Neural Network (FTDNN) to make one-step-ahead ...feed-forward neural networks and time delay neural networks were found to capture the dynamic structure of the rainfall process when ANNs were ... See full document
6
Retrieving Sequential Information for Non Autoregressive Neural Machine Translation
... probability distribution into 5 intervals, and the y-axis indicates the number of times that the top-k probabilities are within this ...probability distribution, and the marginal util- ity for a larger k is ... See full document
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Estimation for autoregressive processes with several unit roots
... Dickey, Hasza and Fuller (1984) obtained similar results for testing the unit roots in seasonal time series.. With the exception of Sen (1985), no one has considered the distribution of [r] ... See full document
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Electricity Consumption Forecasting Using Nonlinear Autoregressive with External (Exogeneous) Input Neural Network
... Forecasting is predicting future values based on historical data. Electricity consumption forecasting is important for Utilities Company to plan for future power generation and distribution. Overestimation of ... See full document
11
Performance of an Optimum Autoregressive Spectral Estimation Method Based on Signal Length
... the distribution of variance of the series as a function of frequency and the objective of spectral analysis is to estimate and study the ...on estimation of the spectrum and efficiency of some spectral ... See full document
6
Estimation of a Type of Form Invariant Combined Signals under Autoregressive Operators
... Copyright © 2013 Yinsheng Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, ... See full document
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Maximum likelihood estimation of higher-order integer-valued autoregressive processes
... Take an arbitrary linear combination of the score l 0 ` _ . Since the score is the sum of a stationary, ergodic martingale di¤erence sequence (with …nite variance), T 1=2 l 0 ` _ automatically satis…es a univariate ... See full document
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