Relevant bibliographies by topics / Non-Autoregressive

Academic literature on the topic 'Non-Autoregressive'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Non-Autoregressive"

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Wang, Shuheng, Shumin Shi, Heyan Huang, and Wei Zhang. "Improving Non-Autoregressive Machine Translation via Autoregressive Training." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012045. http://dx.doi.org/10.1088/1742-6596/2031/1/012045.

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Abstract In recent years, non-autoregressive machine translation has attracted many researchers’ attentions. Non-autoregressive translation (NAT) achieves faster decoding speed at the cost of translation accuracy compared with autoregressive translation (AT). Since NAT and AT models have similar architecture, a natural idea is to use AT task assisting NAT task. Previous works use curriculum learning or distillation to improve the performance of NAT model. However, they are complex to follow and diffucult to be integrated into some new works. So in this paper, to make it easy, we introduce a multi-task framework to improve the performance of NAT task. Specially, we use a fully shared encoder-decoder network to train NAT task and AT task simultaneously. To evaluate the performance of our model, we conduct experiments on serval benchmask tasks, including WMT14 EN-DE, WMT16 EN-RO and IWSLT14 DE-EN. The experimental results demonstrate that our model achieves improvements but still keeps simple.
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Anděl, Jiří. "NON-NEGATIVE AUTOREGRESSIVE PROCESSES." Journal of Time Series Analysis 10, no. 1 (January 1989): 1–11. http://dx.doi.org/10.1111/j.1467-9892.1989.tb00011.x.

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Hong-zhi, An. "NON-NEGATIVE AUTOREGRESSIVE MODELS." Journal of Time Series Analysis 13, no. 4 (July 1992): 283–95. http://dx.doi.org/10.1111/j.1467-9892.1992.tb00108.x.

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Weber, Daniel, and Clemens Gühmann. "Non-Autoregressive vs Autoregressive Neural Networks for System Identification." IFAC-PapersOnLine 54, no. 20 (2021): 692–98. http://dx.doi.org/10.1016/j.ifacol.2021.11.252.

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Tian, Zhengkun, Jiangyan Yi, Jianhua Tao, Shuai Zhang, and Zhengqi Wen. "Hybrid Autoregressive and Non-Autoregressive Transformer Models for Speech Recognition." IEEE Signal Processing Letters 29 (2022): 762–66. http://dx.doi.org/10.1109/lsp.2022.3152128.

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Fei, Zhengcong. "Partially Non-Autoregressive Image Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1309–16. http://dx.doi.org/10.1609/aaai.v35i2.16219.

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Current state-of-the-art image captioning systems usually generated descriptions autoregressively, i.e., every forward step conditions on the given image and previously produced words. The sequential attribution causes a unavoidable decoding latency. Non-autoregressive image captioning, on the other hand, predicts the entire sentence simultaneously and accelerates the inference process significantly. However, it removes the dependence in a caption and commonly suffers from repetition or missing issues. To make a better trade-off between speed and quality, we introduce a partially non-autoregressive model, named PNAIC, which considers a caption as a series of concatenated word groups. The groups are generated parallelly in global while each word in group is predicted from left to right, and thus the captioner can create multiple discontinuous words concurrently at each time step. More importantly, by incorporating curriculum learning-based training tasks of group length prediction and invalid group deletion, our model is capable of generating accurate captions as well as preventing common incoherent errors. Extensive experiments on MS COCO benchmark demonstrate that our proposed method achieves more than 3.5× speedup while maintaining competitive performance.
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Grigoletto, Matteo. "Bootstrap prediction intervals for autoregressive models fitted to non-autoregressive processes." Journal of the Italian Statistical Society 7, no. 3 (December 1998): 285–95. http://dx.doi.org/10.1007/bf03178936.

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Liu, Weidong, Shiqing Ling, and Qi-Man Shao. "On non-stationary threshold autoregressive models." Bernoulli 17, no. 3 (August 2011): 969–86. http://dx.doi.org/10.3150/10-bej306.

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Bell, C. B., and E. P. Smith. "Infrence for non-negative autoregressive schemes." Communications in Statistics - Theory and Methods 15, no. 8 (January 1986): 2267–93. http://dx.doi.org/10.1080/03610928608829248.

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Lii, K. S., and M. Rosenblatt. "Non-Gaussian autoregressive moving average processes." Proceedings of the National Academy of Sciences 90, no. 19 (October 1, 1993): 9168–70. http://dx.doi.org/10.1073/pnas.90.19.9168.

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Dissertations / Theses on the topic "Non-Autoregressive"

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Clayton, Maya. "Econometric forecasting of financial assets using non-linear smooth transition autoregressive models." Thesis, University of St Andrews, 2011. http://hdl.handle.net/10023/1898.

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Following the debate by empirical finance research on the presence of non-linear predictability in stock market returns, this study examines forecasting abilities of nonlinear STAR-type models. A non-linear model methodology is applied to daily returns of FTSE, S&P, DAX and Nikkei indices. The research is then extended to long-horizon forecastability of the four series including monthly returns and a buy-and-sell strategy for a three, six and twelve month holding period using non-linear error-correction framework. The recursive out-of-sample forecast is performed using the present value model equilibrium methodology, whereby stock returns are forecasted using macroeconomic variables, in particular the dividend yield and price-earnings ratio. The forecasting exercise revealed the presence of non-linear predictability for all data periods considered, and confirmed an improvement of predictability for long-horizon data. Finally, the present value model approach is applied to the housing market, whereby the house price returns are forecasted using a price-earnings ratio as a measure of fundamental levels of prices. Findings revealed that the UK housing market appears to be characterised with asymmetric non-linear dynamics, and a clear preference for the asymmetric ESTAR model in terms of forecasting accuracy.
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Liu, Ka-yee. "Bayes and empirical Bayes estimation for the panel threshold autoregressive model and non-Gaussian time series." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B30706166.

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Liu, Ka-yee, and 廖家怡. "Bayes and empirical Bayes estimation for the panel threshold autoregressive model and non-Gaussian time series." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B30706166.

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Nyman, Nick, and Smura Michel Postigo. "Examining how unforeseen events affect accuracy and recovery of a non-linear autoregressive neural network in stock market prognoses." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186435.

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This report studies how a non-linear autoregressive neural network algorithm for stock market value prognoses is affected by unforeseen events. The study attempts to find out the recovery period for said algorithms after an event, and whether the magnitude of the event affects the recovery period. Tests of 1-day prognoses' deviations from the observed value are carried out on five real stock events and four created simulation sets which exclude the noisy data of the stock market and isolates different kinds of events. The study concludes that the magnitude has no discernible impact on recovery, and that a sudden event will allow recovery within days regardless of magnitude or change in price development rate. However, less sudden events will cause the recovery period to extend. Noise such as surrounding micro-events, aftershocks, or lingering instability of stock prices will affect accuracy and recovery time significantly.
Denna studie undersöker hur ett icke-linjärt autoregressivt neuronnät för aktiemarknadsprognoser påverkas av oväntade händelser. Studien ämnar finna återhämtningsperioden för nätverket efter en händelse, och ta reda på om den initiala påverkan av händelsen påverkar återhämtningen. Tester av endagsprognosers avvikelse från det verkliga värdet genomförs på fem verkliga aktier och fyra skapade dataset som exkluderar den omgivande variationen från aktiemarknaden. Dessa simulerade set isolerar därmed specifika typer av händelser. Studien drar slutsatsen att storleken av händelsen har försumbar betydelse på återhämtningstiden och att plötsliga händelser tillåter återhämtning på några dagar oavsett händelsens ursprungliga storlek eller förändring av prisutvecklingshastighet. Däremot förlänger utdragna händelser återhämtningstiden. Likaså påverkar efterskalv eller kvarvarande instabilitet i prisutvecklingen tillförlitlighet och återhämtningstid avsevärt.
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Krisztin, Tamás. "Semi-parametric spatial autoregressive models in freight generation modeling." Elsevier, 2018. https://publish.fid-move.qucosa.de/id/qucosa%3A72336.

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This paper proposes for the purposes of freight generation a spatial autoregressive model framework, combined with non-linear semi-parametric techniques. We demonstrate the capabilities of the model in a series of Monte Carlo studies. Moreover, evidence is provided for non-linearities in freight generation, through an applied analysis of European NUTS-2 regions. We provide evidence for significant spatial dependence and for significant non-linearities related to employment rates in manufacturing and infrastructure capabilities in regions. The non-linear impacts are the most significant in the agricultural freight generation sector.
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Wang, Yuefeng. "Essays on modelling house prices." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16242.

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Housing prices are of crucial importance in financial stability management. The severe financial crises that originated in the housing market in the US and subsequently spread throughout the world highlighted the crucial role that the housing market plays in preserving financial stability. After the severe housing market crash, many financial institutions in the US suffered from high default rates, severe liquidity shortages, and even bankruptcy. Against this background, researchers have sought to use econometric models to capture and forecast prices of homes. Available empirical research indicates that nonlinear models may be suitable for modelling price cycles. Accordingly, this thesis focuses primarily on using nonlinear models to empirically investigate cyclical patterns in housing prices. More specifically, the content of this thesis can be summarised in three essays which complement the existing literature on price modelling by using nonlinear models. The first essay contributes to the literature by testing the ability of regime switching models to capture and forecast house prices. The second essay examines the impact of banking factors on house price fluctuations. To account for house price characteristics, the regime switching model and generalised autoregressive conditionally heteroscedastic (GARCH) in-mean model have been used. The final essay investigates the effect of structural breaks on the unit root test and shows that a time-varying GARCH in-mean model can be used to estimate the housing price cycle in the UK.
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Cugliari, Jairo. "Prévision non paramétrique de processus à valeurs fonctionnelles : application à la consommation d’électricité." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112234/document.

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Nous traitons dans cette thèse le problème de la prédiction d’un processus stochastique à valeurs fonctionnelles. Nous commençons par étudier le modèle proposé par Antoniadis et al. (2006) dans le cadre d’une application pratique -la demande d’énergie électrique en France- où l’hypothèse de stationnarité semble ne pas se vérifier. L’écart du cadre stationnaire est double: d’une part, le niveau moyen de la série semble changer dans le temps, d’autre part il existe groupes dans les données qui peuvent être vus comme des classes de stationnarité.Nous explorons corrections qui améliorent la performance de prédiction. Les corrections visent à prendre en compte la présence de ces caractéristiques non stationnaires. En particulier, pour traiter l’existence de groupes, nous avons contraint le modèle de prévision à n’utiliser que les données qui appartiennent au même groupe que celui de la dernière observation disponible. Si le regroupement est connu, un simple post-traitement suffit pour obtenir des meilleures performances de prédiction.Si le regroupement en blocs est inconnu, nous proposons de découvrir le regroupement en utilisant des algorithmes d’analyse de classification non supervisée. La dimension infinie des trajectoires, pas nécessairement stationnaires, doit être prise en compte par l’algorithme. Nous proposons deux stratégies pour ce faire, toutes les deux basées sur les transformées en ondelettes. La première se base dans l’extraction d’attributs associés à la transformée en ondelettes discrète. L’extraction est suivie par une sélection des caractéristiques le plus significatives pour l’algorithme de classification. La seconde stratégie classifie directement les trajectoires à l’aide d’une mesure de dissimilarité sur les spectres en ondelettes. La troisième partie de la thèse est consacrée à explorer un modèle de prédiction alternatif qui intègre de l’information exogène. A cet effet, nous utilisons le cadre des processus Autorégressifs Hilbertiens. Nous proposons une nouvelle classe de processus que nous appelons processus Conditionnels Autorégressifs Hilbertiens (CARH). Nous développons l’équivalent des estimateurs par projection et par résolvant pour prédire de tels processus
This thesis addresses the problem of predicting a functional valued stochastic process. We first explore the model proposed by Antoniadis et al. (2006) in the context of a practical application -the french electrical power demand- where the hypothesis of stationarity may fail. The departure from stationarity is twofold: an evolving mean level and the existence of groupsthat may be seen as classes of stationarity.We explore some corrections that enhance the prediction performance. The corrections aim to take into account the presence of these nonstationary features. In particular, to handle the existence of groups, we constraint the model to use only the data that belongs to the same group of the last available data. If one knows the grouping, a simple post-treatment suffices to obtain better prediction performances.If the grouping is unknown, we propose it from data using clustering analysis. The infinite dimension of the not necessarily stationary trajectories have to be taken into account by the clustering algorithm. We propose two strategies for this, both based on wavelet transforms. The first one uses a feature extraction approach through the Discrete Wavelet Transform combined with a feature selection algorithm to select the significant features to be used in a classical clustering algorithm. The second approach clusters directly the functions by means of a dissimilarity measure of the Continuous Wavelet spectra.The third part of thesis is dedicated to explore an alternative prediction model that incorporates exogenous information. For this purpose we use the framework given by the Autoregressive Hilbertian processes. We propose a new class of processes that we call Conditional Autoregressive Hilbertian (carh) and develop the equivalent of projection and resolvent classes of estimators to predict such processes
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Hili, Ouagnina. "Contribution à l'estimation des modèles de séries temporelles non linéaires." Université Louis Pasteur (Strasbourg) (1971-2008), 1995. http://www.theses.fr/1995STR13169.

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Le but de la these est d'effectuer l'inference statistique d'une classe generale de modeles de series temporelles non lineaires. Notre contribution consiste d'abord a determiner des conditions assurant l'existence d'une loi stationnaire, l'existence des moments de cette loi stationnaire et la forte melangeance de tels modeles. Nous etablissons ensuite les proprietes asymptotiques de l'estimateur du minimum de distance d'hellinger du parametre d'interet. La robustesse de cet estimateur est egalement envisagee. Nous examinons aussi, via la methode des moindres carres, les proprietes asymptotiques des estimateurs des coefficients des modeles autoregressifs a seuils
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Caron, Nathalie. "Approches alternatives d'une théorie non informative des tests bayésiens." Rouen, 1994. http://www.theses.fr/1994ROUES028.

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Le but de cette thèse est de comparer les réponses classiques (p-values) et bayésiennes dans le cadre d'un test bilatéral et d'introduire de nouvelles réponses bayésiennes non informatives. Elle comprend donc une présentation des réponses classiques et bayésiennes aux différents types de tests sous l'angle de la théorie de la décision. Le deuxième chapitre est consacré à la comparaison des réponses classiques et bayésiennes dans le cadre unidimensionnel en explicitant les critères de choix retenus pour définir une réponse bayésienne objective. Dans les chapitres trois et six, deux nouvelles réponses bayésiennes non informatives sont développées. Les autres chapitres constituent une généralisation du cadre unidimensionnel: les chapitres 4, 5 et 7 généralisent les résultats respectivement au cadre multidimensionnel, au cas d'un test avec des paramètres de nuisance et au cas d'observations corrélées par un modèle autorégressif
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Korale, Asoka Jeevaka Maligaspe. "Non-stationary adaptive signal prediction with error bounds." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326258.

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Books on the topic "Non-Autoregressive"

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Balakrishna, N. Non-Gaussian Autoregressive-Type Time Series. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8162-2.

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Zenner, Markus. Learning to become rational: The case of self-referential autoregressive and non-stationary models. New York: Springer, 1996.

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Balakrishna, N. Non-Gaussian Autoregressive-Type Time Series. Springer Singapore Pte. Limited, 2022.

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Mullen, Thomas. A non-tested hypothesis test of multivariate models with autoregressive processes. 1988.

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Zenner, Markus. Learning to Become Rational: The Case of Self-Referential Autoregressive and Non-Stationary Models. Springer London, Limited, 2013.

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McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.
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Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.
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Book chapters on the topic "Non-Autoregressive"

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Balakrishna, N. "Some Non-linear AR-type Models for Non-Gaussian Time Series." In Non-Gaussian Autoregressive-Type Time Series, 127–54. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8162-2_5.

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Balakrishna, N. "Autoregressive-Type Time Series of Counts." In Non-Gaussian Autoregressive-Type Time Series, 195–225. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8162-2_7.

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Balakrishna, N. "AR Models with Stationary Non-Gaussian Real-Valued Marginals." In Non-Gaussian Autoregressive-Type Time Series, 93–126. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8162-2_4.

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Balakrishna, N. "Linear Time Series Models with Non-Gaussian Innovations." In Non-Gaussian Autoregressive-Type Time Series, 155–94. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8162-2_6.

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Balakrishna, N. "Basics of Time Series." In Non-Gaussian Autoregressive-Type Time Series, 1–17. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8162-2_1.

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Balakrishna, N. "Statistical Inference for Stationary Linear Time Series." In Non-Gaussian Autoregressive-Type Time Series, 19–39. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8162-2_2.

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Balakrishna, N. "AR Models with Stationary Non-Gaussian Positive Marginals." In Non-Gaussian Autoregressive-Type Time Series, 41–92. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8162-2_3.

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Ozaki, T. "Non-Gaussian characteristics of exponential autoregressive processes." In Developments in Time Series Analysis, 257–73. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4515-0_18.

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Ito, Ryuichi, Chuan Xiao, and Makoto Onizuka. "Robust Cardinality Estimator by Non-autoregressive Model." In Communications in Computer and Information Science, 55–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93849-9_3.

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Meriem, Henkouche. "Maximum Likelihood Estimators in Non Linear Autoregressive Processes." In Operations Research ’92, 370–71. Heidelberg: Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-662-12629-5_102.

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Conference papers on the topic "Non-Autoregressive"

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Zhou, Long, Jiajun Zhang, and Chengqing Zong. "Improving Autoregressive NMT with Non-Autoregressive Model." In Proceedings of the First Workshop on Automatic Simultaneous Translation. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.autosimtrans-1.4.

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Gu, Jiatao, and Xu Tan. "Non-Autoregressive Sequence Generation." In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-tutorials.4.

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Bin, Yi, Wenhao Shi, Jipeng Zhang, Yujuan Ding, Yang Yang, and Heng Tao Shen. "Non-Autoregressive Cross-Modal Coherence Modelling." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548184.

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Ma, Yun, and Qing Li. "Exploring Non-Autoregressive Text Style Transfer." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.730.

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Hayashi, Tomoki, Wen-Chin Huang, Kazuhiro Kobayashi, and Tomoki Toda. "Non-Autoregressive Sequence-To-Sequence Voice Conversion." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413973.

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Elias, Isaac, Heiga Zen, Jonathan Shen, Yu Zhang, Ye Jia, Ron J. Weiss, and Yonghui Wu. "Parallel Tacotron: Non-Autoregressive and Controllable TTS." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414718.

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Duprela Tour, Tom, Yves Grenier, and Alexandre Gramfort. "Driver Estimation in Non-Linear Autoregressive Models." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462268.

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Weiran, Wang, Ke Hu, and Tara Sainath. "Streaming Align-Refine for Non-autoregressive Deliberation." In Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-10715.

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Saharia, Chitwan, William Chan, Saurabh Saxena, and Mohammad Norouzi. "Non-Autoregressive Machine Translation with Latent Alignments." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.83.

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Li, Baizhen, Yibin Zhan, Zhihua Wei, Shi Huang, and Lijun Sun. "Improved non-autoregressive dialog state tracking model." In CCRIS'21: 2021 2nd International Conference on Control, Robotics and Intelligent System. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3483845.3483880.

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Reports on the topic "Non-Autoregressive"

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Kay, Steven M., and Debasis Sengupta. Simple and Efficient Estimation of Parameters of Non-Gaussian Autoregressive Processes. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada175395.

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